Ex4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ex4_mainLoss+Diag+Coins.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwRTIXeJqab_"
      },
      "source": [
        "## Basic Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4Zqx3ZBOKnO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e93034ef-8597-4d84-910c-6f95c094bd75"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import networkx as nx\n",
        "\n",
        "from platform import python_version\n",
        "print(\"Python Version\", python_version())\n",
        "print('PyTorch Version', torch.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python Version 3.7.12\n",
            "PyTorch Version 1.10.0+cu111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evkPHCXNqISe",
        "outputId": "c70a6feb-c714-495c-f4fe-47ed5983a0e7"
      },
      "source": [
        "# Device\n",
        "DEVICE = 'cpu'\n",
        "if torch.cuda.is_available():\n",
        "    DEVICE = \"cuda\"\n",
        "print(\"device =\", DEVICE)\n",
        "# Reproducibility\n",
        "SEED = 3407\n",
        "import random\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "# Parallel processing\n",
        "NUM_CORES = 20\n",
        "torch.set_num_threads(NUM_CORES)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "device = cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjVT4mJ8qLpp",
        "outputId": "e186a085-1037-44b5-db8c-fe67bfe0710c"
      },
      "source": [
        "if DEVICE==\"cpu\":\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html\n",
        "else:\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu111.html\n",
        "import torch_geometric as pyg\n",
        "print('PyTorch Geometric Version', pyg.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 291 kB 247 kB/s \n",
            "\u001b[K     |████████████████████████████████| 639 kB 38.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 328 kB 72.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 138 kB 3.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 325 kB 11.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 407 kB 61.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 45 kB 1.9 MB/s \n",
            "\u001b[?25h  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "PyTorch Geometric Version 2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojccKMV1qdrh"
      },
      "source": [
        "## Prepare graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "th2rr-XwqW5z"
      },
      "source": [
        "def to_node_idx(x,y):\n",
        "    return (8*x) + y\n",
        "\n",
        "def to_coord(node_idx):\n",
        "    return node_idx//8, node_idx%8\n",
        "\n",
        "edge_index = [[],[]]\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        if i<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j))\n",
        "            edge_index[0].append(to_node_idx(i+1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if j>0:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j-1))\n",
        "                edge_index[0].append(to_node_idx(i+1,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if i>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j))\n",
        "            edge_index[0].append(to_node_idx(i-1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if j<7:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j+1))\n",
        "                edge_index[0].append(to_node_idx(i-1,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j+1))\n",
        "            edge_index[0].append(to_node_idx(i,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if i<7:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j+1))\n",
        "                edge_index[0].append(to_node_idx(i+1,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j-1))\n",
        "            edge_index[0].append(to_node_idx(i,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if i>0:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j-1))\n",
        "                edge_index[0].append(to_node_idx(i-1,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "\n",
        "edge_index = torch.tensor(edge_index, dtype=torch.long).to(DEVICE)\n",
        "COINS = set([17])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n41CppvVqmv9",
        "outputId": "ddb4855c-6421-45cc-b45a-99663959b24d"
      },
      "source": [
        "data = pyg.data.Data(edge_index = edge_index).to(DEVICE)\n",
        "NUM_NODES = data.num_nodes\n",
        "NUM_EDGES = data.num_edges\n",
        "print(\"No. of nodes =\", NUM_NODES)\n",
        "print(\"No. of edges =\", NUM_EDGES)\n",
        "print(\"edge_index.shape =\", data.edge_index.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of nodes = 64\n",
            "No. of edges = 840\n",
            "edge_index.shape = torch.Size([2, 840])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "RJVtbHoVqnr0",
        "outputId": "97cf98cd-53ad-40ba-8946-048c3b2fe4f2"
      },
      "source": [
        "graph = pyg.utils.to_networkx(data)\n",
        "pos = {}\n",
        "for idx in range(64):\n",
        "    x,y = to_coord(idx)\n",
        "    pos[idx] = np.array([x,y])\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "color = ['y' for i in range(64)]\n",
        "for coin in COINS:\n",
        "    color[coin] = 'r'\n",
        "nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YM9i6NEEqno8"
      },
      "source": [
        "def plot_policy(policy):\n",
        "    u = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    v = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    for state in range(64):\n",
        "        x,y = to_coord(state)\n",
        "        action = np.argmax(policy[state])\n",
        "        if action==0:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = 0.75\n",
        "        elif action==1:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = 0.75\n",
        "        elif action==2:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = 0\n",
        "        elif action==3:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = -0.75\n",
        "        elif action==4:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = -0.75\n",
        "        elif action==5:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = -0.75\n",
        "        elif action==6:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = 0\n",
        "        else:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = 0.75\n",
        "\n",
        "    graph = pyg.utils.to_networkx(data)\n",
        "    pos = {}\n",
        "    for idx in range(64):\n",
        "        x,y = to_coord(idx)\n",
        "        pos[idx] = np.array([x,y])\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(8, 8))\n",
        "    color = ['y' for i in range(64)]\n",
        "    for coin in COINS:\n",
        "        color[coin] = 'r'\n",
        "    nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax, edgelist=[])\n",
        "    \n",
        "    X = np.arange(0,8,1)\n",
        "    Y = np.arange(0,8,1)\n",
        "\n",
        "    ax.quiver(X, Y, u, v, scale_units='xy', scale=1)\n",
        "    \n",
        "    ax.xaxis.set_ticks([])\n",
        "    ax.yaxis.set_ticks([])\n",
        "    ax.set_aspect('equal')\n",
        "    plt.grid()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49yosqtWNi7B"
      },
      "source": [
        "## 3. Define random walker and Initialize policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JWCtmMGjBfr"
      },
      "source": [
        "POLICY = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        POLICY[u] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "        if j<7: # up\n",
        "            POLICY[u][0] = 1.0\n",
        "        if j<7 and i<7: # up-right\n",
        "            POLICY[u][1] = 1.0\n",
        "        if i<7: # right\n",
        "            POLICY[u][2] = 1.0\n",
        "        if i<7 and j>0: # down-right\n",
        "            POLICY[u][3] = 1.0\n",
        "        if j>0: # down\n",
        "            POLICY[u][4] = 1.0\n",
        "        if j>0 and i>0: # down-left\n",
        "            POLICY[u][5] = 1.0\n",
        "        if i>0: # left\n",
        "            POLICY[u][6] = 1.0\n",
        "        if j<7 and i>0: # up-left\n",
        "            POLICY[u][7] = 1.0\n",
        "        POLICY[u] /= sum(POLICY[u])\n",
        "\n",
        "Q = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        Q[u] = {}\n",
        "        if j<7: # up\n",
        "            Q[u][0] = torch.rand(1)\n",
        "        if j<7 and i<7: # up-right\n",
        "            Q[u][1] = torch.rand(1)\n",
        "        if i<7: # right\n",
        "            Q[u][2] = torch.rand(1)\n",
        "        if i<7 and j>0: # down-right\n",
        "            Q[u][3] = torch.rand(1)\n",
        "        if j>0: # down\n",
        "            Q[u][4] = torch.rand(1)\n",
        "        if j>0 and i>0: # down-left\n",
        "            Q[u][5] = torch.rand(1)\n",
        "        if i>0: # left\n",
        "            Q[u][6] = torch.rand(1)\n",
        "        if j<7 and i>0: # up-left\n",
        "            Q[u][7] = torch.rand(1)\n",
        "\n",
        "RET = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        RET[u] = [[], [], [], [], [], [], [], []]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3eBev53qnmX"
      },
      "source": [
        "def sample_episode(walk_len, start_node=None):\n",
        "    if start_node==None:\n",
        "        start_node = np.random.randint(0,NUM_NODES)\n",
        "    states = []\n",
        "    actions = []\n",
        "    rewards = []\n",
        "    curr_state = start_node\n",
        "    while len(states) < walk_len:\n",
        "        states.append(curr_state)\n",
        "        if curr_state in COINS:\n",
        "            rewards.append(1.0)\n",
        "        else:\n",
        "            rewards.append(0.0)\n",
        "        actions.append(np.random.choice(a=[0,1,2,3,4,5,6,7], size=None, replace=True, p=POLICY[curr_state]))\n",
        "        if actions[-1]==0:      # up\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]+1)\n",
        "        elif actions[-1]==1:      # up-right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1]+1)\n",
        "        elif actions[-1]==2:    # right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1])\n",
        "        elif actions[-1]==3:      # down-right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==4:    # down\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==5:      # down-left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==6:    # left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1])\n",
        "        else:      # up-left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1]+1)\n",
        "    return states, actions, rewards"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_MlK2uxrJpM"
      },
      "source": [
        "## Build models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6FJwSCOqnj-"
      },
      "source": [
        "# Set Hyperparameters\n",
        "EMBED_DIM = 512\n",
        "EPS = 1e-15\n",
        "INIT_LR1 = 0.1\n",
        "\n",
        "WALK_LEN = 128\n",
        "INIT_LR2 = 1e-3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgWVxsR1qnhh"
      },
      "source": [
        "class Node2Vec(torch.nn.Module):\n",
        "    def __init__(self, embedding_dim=EMBED_DIM, num_nodes=NUM_NODES, sparse=False):\n",
        "        super(Node2Vec, self).__init__()\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.embedding = torch.nn.Embedding(num_nodes, embedding_dim, sparse=sparse)\n",
        "        self.reset_parameters()\n",
        "\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        self.embedding.reset_parameters()\n",
        "\n",
        "\n",
        "    def forward(self, batch=None):\n",
        "        emb = self.embedding.weight\n",
        "        return emb if batch is None else emb[batch]\n",
        "\n",
        "\n",
        "    def loss(self, pos_rw, neg_rw):\n",
        "        start, rest = pos_rw[:, 0], pos_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(pos_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(pos_rw.size(0), -1, self.embedding_dim)\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        pos_loss = -torch.log(torch.sigmoid(out) + EPS).mean()\n",
        "\n",
        "        start, rest = neg_rw[:, 0], neg_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(neg_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(neg_rw.size(0), -1, self.embedding_dim)\n",
        "\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        neg_loss = -torch.log(1 - torch.sigmoid(out) + EPS).mean()\n",
        "        return pos_loss + neg_loss\n",
        "\n",
        "model1 = Node2Vec().to(DEVICE)\n",
        "optimizer1 = torch.optim.Adam(model1.parameters(), INIT_LR1)\n",
        "platLR1 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer1, mode='min', factor=0.5, patience=1, threshold=0.0001, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DvylOvFqneX"
      },
      "source": [
        "def get_contexts(walks, size):\n",
        "    batch = torch.tensor([], dtype=torch.int, requires_grad=False).to(DEVICE)\n",
        "    for sequence in walks:\n",
        "        for i in range(0,len(sequence)-size):\n",
        "            batch = torch.cat((batch, sequence[i:i+size].reshape(1,size)), axis=0)\n",
        "    return batch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_ZEVaearYQI"
      },
      "source": [
        "class InferNet(torch.nn.Module):\n",
        "    def __init__ (self, input_len=EMBED_DIM):\n",
        "        super(InferNet, self).__init__()\n",
        "        self.input_size = input_len\n",
        "        self.lin1 = torch.nn.Linear(self.input_size,512)\n",
        "        self.lin4 = torch.nn.Linear(512,1)\n",
        "\n",
        "    def forward(self, X):\n",
        "        X = torch.tanh(self.lin1(X))\n",
        "        X = self.lin4(X)\n",
        "        return X\n",
        "\n",
        "model2 = InferNet().to(DEVICE)\n",
        "optimizer2 = torch.optim.Adam(model2.parameters(), INIT_LR2)\n",
        "platLR2 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer2, mode='min', factor=0.5, patience=1, threshold=0.0001, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tKwUWa8rYNv"
      },
      "source": [
        "mse = torch.nn.MSELoss(reduction='sum')\n",
        "def getLoss2(imm_rewards, rewards):\n",
        "    main_loss = mse(torch.sum(imm_rewards), torch.sum(rewards)) / WALK_LEN\n",
        "    return main_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Q1o1QIrYA-"
      },
      "source": [
        "def printRewards(rewards):\n",
        "    matrix = np.array([[float(rewards[to_node_idx(i,j)][0]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"blue\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqK-2Z1Ur6sT"
      },
      "source": [
        "## Train models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Zppsa_AgrYI4",
        "outputId": "d438f1bd-2539-4141-a2d4-784cb9306761"
      },
      "source": [
        "NUM_ITER = 50\n",
        "CONT_SIZE = 3\n",
        "NUM_NEG = 1\n",
        "losses1 = []\n",
        "losses2 = []\n",
        "# GAMMA = 0.5\n",
        "\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "printRewards(imm_rewards)\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    neg_samples = torch.randint(low=0, high=NUM_NODES, size=(NUM_NODES,(WALK_LEN*NUM_NEG)-1))\n",
        "    neg_samples = neg_samples.reshape(NUM_NODES, (WALK_LEN*NUM_NEG)-1)\n",
        "\n",
        "    states = torch.tensor(states).to(DEVICE)\n",
        "    actions = torch.tensor(actions).to(DEVICE)\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    neg_samples = torch.cat((states[:,0].reshape(NUM_NODES,1), neg_samples.to(DEVICE)), axis=1)\n",
        "\n",
        "    model1.train()\n",
        "    optimizer1.zero_grad()\n",
        "    pos = get_contexts(states, CONT_SIZE)\n",
        "    neg = get_contexts(neg_samples, CONT_SIZE)\n",
        "    loss1 = model1.loss(pos.to(DEVICE), neg.to(DEVICE))\n",
        "    loss1.backward()\n",
        "    optimizer1.step()\n",
        "    loss1 = float(loss1)\n",
        "    platLR1.step(loss1)\n",
        "    \n",
        "    model2.train()\n",
        "    loss2 = 0.0\n",
        "    for i in range(NUM_NODES):\n",
        "        optimizer2.zero_grad()\n",
        "        ep_states = model1.forward(states[i,:].reshape(WALK_LEN)).reshape(WALK_LEN,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(ep_states).reshape(WALK_LEN,1)\n",
        "        L = getLoss2(imm_rewards, rewards[i].reshape(WALK_LEN,1))\n",
        "        L.backward()\n",
        "        optimizer2.step()\n",
        "        loss2 += float(L)\n",
        "    loss2 /= float(NUM_NODES)\n",
        "    platLR2.step(loss2)\n",
        "\n",
        "    # if len(losses1)>0:\n",
        "    #     loss1 = (GAMMA*loss1) + ((1-GAMMA)*losses1[-1])\n",
        "    # if len(losses2)>0:\n",
        "    #     loss2 = (GAMMA*loss2) + ((1-GAMMA)*losses2[-1])\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  loss1 : \"+str(np.around(loss1,6))+\" |  loss2 : \"+str(np.around(loss2,6)))\n",
        "    losses1.append(loss1)\n",
        "    losses2.append(loss2)\n",
        "\n",
        "    if iter in [10, 25, 50]:\n",
        "        model2.eval()\n",
        "        all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "        printRewards(imm_rewards)"
      ],
      "execution_count": null,
      "outputs": [
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              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.112307</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.077897</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.056860</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >-0.193183</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_057f9546_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.217227</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.449049</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.253179</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.222002</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >-0.027036</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.246256</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.162212</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >-0.286815</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_057f9546_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.506386</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.082366</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.306199</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >-0.037394</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.165799</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.143218</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.138173</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >-0.189759</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_057f9546_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >-0.488101</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.006839</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.022729</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.290967</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.300161</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >-0.069967</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.169547</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.211054</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_057f9546_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.007664</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >-0.277316</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.244599</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.205027</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.196127</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.139028</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.417372</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.360891</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_057f9546_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.194938</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.070811</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.375516</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.089531</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.094787</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.238499</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >-0.115760</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.220434</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_057f9546_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.131425</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.726761</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.018675</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >-0.322981</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.216040</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.199119</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.626912</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.070740</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_057f9546_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.025977</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.212268</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.275828</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.270882</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.411360</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.278527</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.187398</td>\n",
              "                        <td id=\"T_057f9546_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.193604</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda1f1c55d0>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 1 |  loss1 : 18.854073 |  loss2 : 12.300683\n",
            "iter : 2 |  loss1 : 13.683125 |  loss2 : 1.150012\n",
            "iter : 3 |  loss1 : 12.18894 |  loss2 : 0.330419\n",
            "iter : 4 |  loss1 : 11.846164 |  loss2 : 0.119316\n",
            "iter : 5 |  loss1 : 11.615488 |  loss2 : 0.068906\n",
            "iter : 6 |  loss1 : 11.182859 |  loss2 : 0.029056\n",
            "iter : 7 |  loss1 : 10.946642 |  loss2 : 0.026057\n",
            "iter : 8 |  loss1 : 10.730476 |  loss2 : 0.034975\n",
            "iter : 9 |  loss1 : 10.536672 |  loss2 : 0.029465\n",
            "iter : 10 |  loss1 : 10.206419 |  loss2 : 0.019949\n"
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              "        }#T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col6{\n",
              "            background-color:  #dcdcf4;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >-0.025599</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.098550</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.039616</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.041869</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.060824</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.043192</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.055674</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.060579</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.137312</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.107441</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.049625</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >-0.000547</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >-0.027841</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.094500</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.025160</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >-0.109358</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.143076</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.129554</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.036901</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.149418</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.005821</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.007285</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >-0.070087</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.006618</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >-0.193108</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >-0.083373</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >-0.077673</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.090036</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.054644</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.047841</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.015977</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.112855</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.079586</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.068967</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >-0.075778</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.108166</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.078921</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.024365</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >-0.090129</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.056049</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.189701</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.269889</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.055104</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.103138</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.056203</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >-0.028976</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.023484</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.025792</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >-0.013069</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.076566</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.573951</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >-0.024376</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.056508</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.061112</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.029582</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.000169</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >-0.035053</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.053658</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.289590</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.028848</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.129465</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.100036</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.126387</td>\n",
              "                        <td id=\"T_12bcff6e_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.040126</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda1f1b7c10>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 11 |  loss1 : 10.105074 |  loss2 : 0.014261\n",
            "iter : 12 |  loss1 : 9.96014 |  loss2 : 0.007274\n",
            "iter : 13 |  loss1 : 10.023799 |  loss2 : 0.012561\n",
            "iter : 14 |  loss1 : 10.015869 |  loss2 : 0.008013\n",
            "iter : 15 |  loss1 : 9.715332 |  loss2 : 0.006922\n",
            "iter : 16 |  loss1 : 9.581656 |  loss2 : 0.004481\n",
            "iter : 17 |  loss1 : 9.396087 |  loss2 : 0.006196\n",
            "iter : 18 |  loss1 : 9.420031 |  loss2 : 0.003\n",
            "iter : 19 |  loss1 : 9.27585 |  loss2 : 0.003864\n",
            "iter : 20 |  loss1 : 9.214089 |  loss2 : 0.003181\n",
            "iter : 21 |  loss1 : 9.250169 |  loss2 : 0.002916\n",
            "iter : 22 |  loss1 : 9.273164 |  loss2 : 0.00313\n",
            "iter : 23 |  loss1 : 9.130704 |  loss2 : 0.002045\n",
            "iter : 24 |  loss1 : 9.053638 |  loss2 : 0.00298\n",
            "iter : 25 |  loss1 : 9.410683 |  loss2 : 0.002119\n"
          ]
        },
        {
          "output_type": "display_data",
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              "            background-color:  #a3a3f7;\n",
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              "            background-color:  #cacaf5;\n",
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              "            background-color:  #cecef5;\n",
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              "        }</style><table id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >-0.061295</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.064055</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.026037</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >-0.011336</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.038791</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.050276</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.016627</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.048652</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.100165</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.005923</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.028165</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >-0.011378</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.019639</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.048940</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.015398</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >-0.052404</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.009111</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.078434</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.048519</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.069500</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.006535</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.008801</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >-0.005465</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.065164</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >-0.085477</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.003806</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >-0.063554</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.026870</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.058698</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.033356</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.025183</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.035202</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.023672</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.077518</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >-0.037862</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.050292</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.005318</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >-0.001861</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >-0.018322</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.010069</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.135966</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.168449</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.034425</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.043234</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.012308</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >-0.023861</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.040300</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.006370</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
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              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.043819</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.798352</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.022770</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.019097</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.031936</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.021827</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.040761</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >-0.018402</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.031809</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.166106</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.016558</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.025159</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.000081</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.011264</td>\n",
              "                        <td id=\"T_23c8aa88_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >-0.003912</td>\n",
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              "    </tbody></table>"
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          "text": [
            "iter : 26 |  loss1 : 8.828151 |  loss2 : 0.001999\n",
            "iter : 27 |  loss1 : 9.114286 |  loss2 : 0.00181\n",
            "iter : 28 |  loss1 : 9.12457 |  loss2 : 0.002457\n",
            "iter : 29 |  loss1 : 9.214431 |  loss2 : 0.002398\n",
            "iter : 30 |  loss1 : 8.875691 |  loss2 : 0.0014\n",
            "iter : 31 |  loss1 : 8.907941 |  loss2 : 0.001563\n",
            "iter : 32 |  loss1 : 8.877954 |  loss2 : 0.001998\n",
            "iter : 33 |  loss1 : 9.084884 |  loss2 : 0.001335\n",
            "iter : 34 |  loss1 : 8.842265 |  loss2 : 0.001388\n",
            "iter : 35 |  loss1 : 8.853831 |  loss2 : 0.00158\n",
            "iter : 36 |  loss1 : 8.732239 |  loss2 : 0.001676\n",
            "iter : 37 |  loss1 : 8.685324 |  loss2 : 0.001714\n",
            "iter : 38 |  loss1 : 8.829359 |  loss2 : 0.001809\n",
            "iter : 39 |  loss1 : 8.813309 |  loss2 : 0.001774\n",
            "iter : 40 |  loss1 : 8.839279 |  loss2 : 0.001601\n",
            "iter : 41 |  loss1 : 8.984372 |  loss2 : 0.001879\n",
            "iter : 42 |  loss1 : 8.931166 |  loss2 : 0.001856\n",
            "iter : 43 |  loss1 : 8.774843 |  loss2 : 0.001249\n",
            "iter : 44 |  loss1 : 8.914617 |  loss2 : 0.001552\n",
            "iter : 45 |  loss1 : 8.895618 |  loss2 : 0.001563\n",
            "iter : 46 |  loss1 : 9.024599 |  loss2 : 0.002293\n",
            "iter : 47 |  loss1 : 8.867478 |  loss2 : 0.001477\n",
            "iter : 48 |  loss1 : 8.628921 |  loss2 : 0.001451\n",
            "iter : 49 |  loss1 : 8.925559 |  loss2 : 0.002203\n",
            "iter : 50 |  loss1 : 8.641019 |  loss2 : 0.001708\n"
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              "            background-color:  #c5c5f5;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col6,#T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col5{\n",
              "            background-color:  #d3d3f4;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col0,#T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col6{\n",
              "            background-color:  #d2d2f4;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col1{\n",
              "            background-color:  #bcbcf5;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col3,#T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col6{\n",
              "            background-color:  #c3c3f5;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col4,#T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col5{\n",
              "            background-color:  #cecef4;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col0{\n",
              "            background-color:  #f0f0f3;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col1{\n",
              "            background-color:  #a6a6f7;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col0{\n",
              "            background-color:  #d8d8f4;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col1{\n",
              "            background-color:  #c2c2f5;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col2{\n",
              "            background-color:  #0000ff;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col5{\n",
              "            background-color:  #d5d5f4;\n",
              "            color:  #000000;\n",
              "        }#T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col2{\n",
              "            background-color:  #a7a7f7;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >-0.053245</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.062039</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.019715</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >-0.012046</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.035935</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.046957</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.014117</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.051194</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.086882</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.014510</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.026012</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >-0.014553</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.015394</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.041362</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.005169</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >-0.044966</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.017905</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.087691</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.042566</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.063886</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.003878</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.012387</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.001338</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.075422</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >-0.081142</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.009197</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >-0.061415</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.021168</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.055481</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.032463</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.019974</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.036034</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.015835</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.071170</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >-0.043425</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.041715</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >-0.003571</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.007499</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >-0.016718</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.009496</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.135828</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.152613</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.044203</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.032021</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.013423</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >-0.021715</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.041349</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.004602</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >-0.038433</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.044788</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.795144</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.015844</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.004112</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.029070</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.017769</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.043781</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >-0.011203</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.032598</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.149946</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.004037</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.014422</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.001337</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.012725</td>\n",
              "                        <td id=\"T_403f8a24_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >-0.008570</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda1d66b850>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "NpSZTqB_rYGL",
        "outputId": "5f96eb66-1ea7-4174-a290-65742b0a0035"
      },
      "source": [
        "printRewards(rewards[:,0].reshape(NUM_NODES,1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "            background-color:  #f0f0f3;\n",
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              "                <tr>\n",
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              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >1.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.000000</td>\n",
              "                        <td id=\"T_4045ac38_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda30d0a810>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "LjyGbLSKsa1K",
        "outputId": "e6dfc8a5-1ea3-4eb7-f112-435693e750df"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, losses1, 'r', label='Node2Vec Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Node2Vec Loss vs Iterations\")\n",
        "fig.savefig('Node2VecLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "V1gwNscPs3jJ",
        "outputId": "f4aa153c-3467-4174-bcee-59f6c579505a"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, list(losses2), 'g', label='InferNet Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"InferNet Loss vs Iterations\")\n",
        "fig.savefig('InferNetLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BizZ1n8w8BzgHah8yMpEPFdjchxAM3TcEd8CBBx/IG/Z8wwRG3Zn8QIfmcS6bw3lsHueyeaZyLqc83BFxMnAM8KZswREA3FUuSSqzKQ13RBwNfAL4vzLzualc9wjDLUkqs23552AXAz8HFkTE6oj4IPBVYDbwo4i4LSK+vq3WvymGW5JUZttsizsz3zPO1d/YVutrVLXiscolSeXVsUdOM9ySpDIy3JIklUjHhtvP45YklVHHhtstbklSGRluSZJKxHBLklQiHRfuapd/DiZJKq+OC3elq0IlKoZbklRKHRduqO0uN9ySpDIy3JIklYjhliSpRDoy3NVK1XBLkkqpI8PtFrckqawMtyRJJdKx4fZY5ZKkMurYcLvFLUkqI8MtSVKJGG5JkkrEcEuSVCKGW5KkEjHckiSViOGWJKlEDLckSSXSkeGudnmscklSOXVkuN3iliSVVceGe2DIQ55KksqnY8PtFrckqYwMtyRJJdKx4R4YHiAzWz0USZImpGPDDfjRnpKk0unocLu7XJJUNoZbkqQSMdySJJWI4ZYkqUQMtyRJJdKR4a52VQHDLUkqn44Mt1vckqSy6uhwe7xySVLZdHS43eKWJJWN4ZYkqUQMtyRJJWK4JUkqEcMtSVKJGG5JkkrEcEuSVCKGW5KkEjHckiSVSEeGu1rxWOWSpHLqyHC7xS1JKquODvfAsMcqlySVS0eGuxIVgnCLW5JUOh0Z7oigp9JjuCVJpdOR4QYMtySplAy3JEklYrglSSqRbRbuiDgvItZExB111+0SET+KiHuK0znbav1bYrglSWW0Lbe4zweOHnPdp4AfZ+argB8Xl1vCcEuSymibhTszrwWeHHP1scAFxfkLgOO21fq3xHBLkspoql/jnpuZDxfnHwHmTvH6RxluSVIZdbdqxZmZEZGbuj0iTgVOBZg7dy69vb0NL7uvr2+L91//7Hoeef6RCS23EzUyl2qMc9kczmPzOJfNM5VzOdXhfjQiXpqZD0fES4E1m7pjZp4DnAOwZMmSXLp0acMr6e3tZUv33+3+3ah2Vbd4v07XyFyqMc5lcziPzeNcNs9UzuVU7yr/LvCB4vwHgO9M8fpH9VR6PFa5JKl0tuWfg10M/BxYEBGrI+KDwNnAH0TEPcCRxeWW8DVuSVIZbbNd5Zn5nk3c9KZttc6JMNySpDLyyGmSJJWI4ZYkqUQMtyRJJdK54e4y3JKk8unccLvFLUkqIcMtSVKJGG5JkkqkY8NdrVTZMLSBzE0eLl2SpLbTseHuqfQAMDg82OKRSJLUuI4Pt8crlySVSceH29e5JUllYrgNtySpRAy34ZYklYjhNtySpBIx3IZbklQihttwS5JKxHAbbklSiRhuwy1JKpGODXe1qwoYbklSuXRsuN3iliSVkeE23JKkEun4cA8MeaxySVJ5dHy43eKWJJWJ4TbckqQSMdyGW5JUIobbcEuSSsRwG25JUokYbsMtSSoRw224JUkl0rHh7u7qBgy3JKlcOjbcEUG1q2q4JUml0rHhhtrucsMtSSqTjg/3wLCHPJUklUfHh9stbklSmRhuwy1JKhHDbbglSSViuA23JKlEDLfhliSViOE23JKkEjHchluSVCKG23BLkkrEcBtuSVKJdHS4qxWPVS5JKpeODrdb3JKksun4cHuscklSmXR8uN3iliSVSWeHu8twS5LKpbPD7Ra3JKlkDLfhliSViOE23JKkEjHchluSVCIdH+7hHGZoeKjVQ5EkqSEdH27ArW5JUmkYbgy3JKk8Ojrc1UoVMNySpPJoSbgj4uMRcWdE3BERF0fEjFaMwy1uSVLZTHm4I2IP4KPAksw8AKgA757qccAL4fZ45ZKksmjVrvJuYGZEdAPbA79rxSDc4pYklc2UhzszHwL+Gvgt8DDwdGZePdXjAMMtSSqfyMypXWHEHOBy4F3AWuA/gMsy88Ix9zsVOBVg7ty5r77kkksaXkdfXx+zZs3a4v2uf/x6Pn3np/nnQ/6ZfWbv0/iT6CCNzqW2zLlsDuexeZzL5mn2XC5btmx5Zi4Z77bupq2lcUcC92fmYwAR8S3g94GNwp2Z5wDnACxZsiSXLl3a8Ap6e3tp5P799/bDnXDQ4oM4bN5hDS+/kzQ6l9oy57I5nMfmcS6bZyrnshWvcf8WOCwito+IAN4ErGzBONxVLkkqnVa8xn0DcBlwC/CrYgznTPU4wHBLksqnFbvKyczPAp9txbrrGW5JUtl09JHTDLckqWwMN4ZbklQeHR3uapfHKpcklUtHh9stbklS2RhuYGDIY5VLksrBcOMWtySpPAw3hluSVB6GG8MtSSqPjg53teK7yiVJ5dLR4e6KLrq7ug23JKk0OjrcUNtdbrglSWVhuA23JKlEDLfhliSViOE23JKkEun4cFe7qmwYNtySpHLo+HC7xS1JKhPDXenxWOWSpNIw3G5xS5JKxHAbbklSiRhuwy1JKhHDbbglSSViuA23JKlEDLfhliSViOE23JKkEjHchluSVCKG23BLkkqk48Nd7aoabklSaXR8uHsqPQwMe8hTSVI5GG53lUuSSsRwG25JUokY7koPg8ODDOdwq4ciSdIWGe5KD4Af7SlJKgXDXYTb3eWSpDIw3IZbklQihttwS5JKxHAbbklSiRhuwy1JKhHDbbglSSXS8eGuVqqA4ZYklUPHh3v077g9XrkkqQQMt7vKJUklYrgNtySpRAy34ZYklYjhNtySpBIx3IZbklQihttwS5JKxHAbbklSiTQU7oj4WETsGDXfiIhbIuKobT24qWC4JUll0ugW9ymZ+QxwFDAHeB9w9jYb1RQy3JKkMmk03FGcvhX4t8y8s+66UjPckqQyaTTcyyPiamrhvioiZgPD225YU6fa5bHKJUnl0d3g/T4ILALuy8znImIX4P/edsOaOqPHKh/yWOWSpPbX6Bb364C7M3NtRJwEfBp4etsNa+pUuip0RZdb3JKkUmg03P8LeC4iDgZOA34D/Os2G9UU66n0GG5JUik0Gu7BzEzgWOCrmflPwOxtN6ypZbglSWXRaLjXRcTp1P4M7AcR0QVUJ7vSiNg5Ii6LiLsiYmVEvG6yy2oGwy1JKotGw/0u4Hlqf8/9CDAP+PJWrPcrwA8zcyFwMLByK5a11Qy3JKksGgp3EeuLgJ0i4higPzMn9Rp3ROwEvBH4RrHsDZm5djLLapaeSg8bhg23JKn9NXrI03cCNwL/FXgncENEnDDJde4FPAb8fxFxa0ScGxE7THJZTeEWtySpLKL2nrMt3CniduAPMnNNcXl34H9n5sETXmHEEuAXwOGZeUNEfAV4JjPPGHO/U4FTAebOnfvqSy65pOF19PX1MWvWrIbvf8rNpzBv5jy+sP8XGn5Mp5joXGrTnMvmcB6bx7lsnmbP5bJly5Zn5pLxbmv0ACxdI9EuPMHkP1lsNbA6M28oLl8GfGrsnTLzHOAcgCVLluTSpUsbXkFvby8Tuf+cX89hp9k7TegxnWKic6lNcy6bw3lsHueyeaZyLhsN9w8j4irg4uLyu4ArJ7PCzHwkIh6MiAWZeTfwJmDFZJbVLO4qlySVRUPhzsw/j4h3AIcXV52TmVdsxXo/AlwUET3AfbT48KnVrqrhliSVQqNb3GTm5cDlzVhpZt4GjLvvvhV6Kj30D/a3ehiSJG3RZsMdEeuA8d69FkBm5o7bZFRTrKfSwzPPP9PqYUiStEWbDXdmTpvDmm6Or3FLkspisu8Mn1YMtySpLAw3hluSVB6GG8MtSSoPw43hliSVh+HGcEuSysNwY7glSeVhuDHckqTyMNzUwj0wPEAjn5QmSVIrGW5qxyoHGBgeaPFIJEnaPMNNbYsbYGDIcEuS2pvh5oVw+zq3JKndGW4MtySpPAw3hluSVB6GG8MtSSoPw43hliSVh+HGcEuSysNwY7glSeVhuDHckqTyMNwYbklSeRhuDLckqTwMN1Ct1I5VbrglSe3OcFN3rHI/ZESS1OYMN+4qlySVh+HGcEuSysNwY7glSeVhuDHckqTyMNwYbklSeRhuDLckqTwMN4ZbklQehhuoRIUgDLckqe0ZbiAi6Kn0GG5JUtsz3AXDLUkqA8NdqFaqDAx5yFNJUnsz3AW3uCVJZWC4Cz2VHjYMG25JUnsz3AW3uCVJZWC4C4ZbklQGhrtguCVJZWC4C4ZbklQGhrtguCVJZWC4C4ZbklQGhrtguCVJZWC4C4ZbklQGhrtguCVJZWC4C9Uuj1UuSWp/hrvgFrckqQwMd8FwS5LKwHAXDLckqQwMd8FwS5LKwHAXDLckqQwMd2Ek3JnZ6qFIkrRJhrvQU+khSYZyqNVDkSRpkwx3oafSA+DucklSWzPcBcMtSSqDloU7IioRcWtEfL9VY6hnuCVJZdDKLe6PAStbuP6NGG5JUhm0JNwRMQ94G3BuK9Y/nmpXFcDjlUuS2lp3i9b798AngNmbukNEnAqcCjB37lx6e3sbXnhfX9+E7g9w75p7Abju59fxwPYPTOix09lk5lLjcy6bw3lsHueyeaZyLqc83BFxDLAmM5dHxNJN3S8zzwHOAViyZEkuXbrJu75Ib28vE7k/wBMrnoCVsPjVizlw7oETeux0Npm51Picy+ZwHpvHuWyeqZzLVuwqPxx4e0SsAi4BjoiIC1swjo34GrckqQymPNyZeXpmzsvM+cC7gZ9k5klTPY6xDLckqQz8O+6C4ZYklUGr3pwGQGb2Ar2tHMMIwy1JKgO3uAuGW5JUBoa7YLglSWVguAuGW5JUBoa7YLglSWVguAuGW5JUBoa7UK0Uxyof9ljlkqT2ZbgLbnFLksrAcBcMtySpDAx3wXBLksrAcBdGPo/bcEuS2pnhLkQE1a6q4ZYktTXDXaen0mO4JUltzXDXMdySpHZnuOsYbklSuzPcdQy3JKndGe46hluS1O4Mdx3DLUlqd4a7TrVS9VjlkqS2ZrjruMUtSWp3hruO4ZYktTvDXcdwS5LaneGuY7glSe3OcNcx3JKkdme46xhuSVK7M9x1DLckqd0Z7jqGW5LU7gx3nZ4uwy1Jam+Gu45b3JKkdme46/RUehgY8pCnkqT2ZbjrVCtVt7glSW3NcNdxV7kkqd0Z7jo9lR6Gcoih4aFWD0WSpHEZ7jo9lR4AP9pTktS2DHedkXC7u1yS1K4Mdx3DLUlqd4a7juGWJLU7w13HcEuS2p3hrmO4JUntznDXMdySpHZnuOsYbklSuzPcdUb/jtvjlUuS2pThrlPtqgJucUuS2pfhruOucklSuzPcdQy3JKndGe46hluS1O4Mdx3DLUlqd4a7juGWJLU7w13HcEuS2p3hrmO4JUntznDXMdySpHZnuOsYbklSuzPcdQy3JKndGe461UrtkKcDwx6rXJLUngx3na7oohIVt7glSW3LcI/RU+kx3JKktjXl4Y6Il0fETyNiRUTcGREfm+oxbI7hliS1s+4WrHMQOC0zb4mI2cDyiPhRZq5owVhexHBLktrZlG9xZ+bDmXlLcX4dsBLYY6rHsSmGW5LUzlr6GndEzAcWAze0chz1DLckqZ1FZrZmxRGzgGuAMzPzW+PcfipwKsDcuXNffckllzS87L6+PmbNmjWpcb3/xvez96y9+cx+n5nU46ebrZlLbcy5bA7nsXmcy+Zp9lwuW7ZseWYuGe+2VrzGTURUgcuBi8aLNkBmngOcA7BkyZJcunRpw8vv7e1lIvevt/PKndl5l50n/fjpZmvmUhtzLpvDeWwe57J5pnIuW/Gu8gC+AazMzL+d6vVvibvKJUntrBWvcR8OvA84IiJuK77e2oJxjMtwS5La2ZTvKs/MnwEx1ettlOGWJLUzj5w2Rk+lx2OVS5LaluEeo1qpusUtSWpbhnsMd5VLktqZ4R7DcEuS2pnhHsNwS5LameEeo6fLcEuS2pfhHsMtbklSOzPcYxhuSVI7M9xjGG5JUjsz3GMYbklSOzPcY/RUehgcHmQ4h1s9FEmSXsRwj9FT6QFgYMjDnkqS2o/hHmM03B6vXJLUhgz3GNVKFcDXuSVJbclwjzGyxW24JUntyHCPYbglSe3McI9huCVJ7cxwj2G4JUntzHCPYbglSe3McI9huCVJ7cxwj2G4JUntzHCPMSKGH+EAAA2hSURBVBLu5wefb/FIJEl6McM9xrwd5wFw52N3tngkkiS9mOEe45VzXsn+u+/PFXdd0eqhSJL0IoZ7HMcvPJ5rH7iWJ557otVDkSRpI4Z7HMctPI7hHOZ7v/5eq4ciSdJGDPc4DnnpIbx8x5fz7bu+3eqhSJK0EcM9jojguIXHcdVvruLZDc+2ejiSJI0y3Jtw/MLj6R/s5+rfXN3qoUiSNMpwb8Ib9nwDu8zcxXeXS5LaiuHehO6ubv5wnz/k+7/+PgNDA60ejiRJgOHerOMWHsdT/U9x7QPXtnookiQBhnuzjvq9o5jZPdN3l0uS2obh3oztq9vz5r3fzLfv/jaZ2erhSJJkuLfkuAXHsfqZ1Sx/eHmrhyJJkuHekmP2OYZKVNxdLklqC4Z7C3bdflfeuOcb/bMwSVJbMNwNOH7h8ax4bAW/fuLXrR6KJKnDGe4GHLvwWAB3l0uSWs5wN+AVO72CV7/01YZbktRyhrtBxy08jp+v/jkPr3u41UORJHUww92g4xceD8B37/5ui0ciSepkhrtB++2+H3vvsrfvLpcktZThblBEcPzC4/nJ/T/h6f6nWz0cSVKHMtwTcNzC4xgYHuDKe65s9VAkSR3KcE/AYfMOY+4Oc/n23b67XJLUGoZ7Arqii2MXHMuV91xJ/2B/q4cjSepAhnuCjt/3ePo29PGT+3/S6qFIkjqQ4Z6gZfOXscvMXfiTK/+EXz76y1YPR5LUYQz3BG3XvR0/PPGHbBjawO9/4/e5YqV/HiZJmjqGexJes8druOm/3cT+L9mfP7r0j/ira/6KzGz1sCRJHcBwT9LLZr+Ma06+hvcf/H4+0/sZ3nnZO3l2w7OtHpYkaZoz3FthRvcMzj/2fP7mqL/hWyu/xeHnHc4Dax9o9bAkSdOY4d5KEcGfvu5P+cF7f8Cqtat4zb+8huseuK7Vw5IkTVOGu0mO3vtobvjQDcyZOYc3/eub+Fzv5+hd1cva/rWtHpokaRrpbsVKI+Jo4CtABTg3M89uxTiabcFuC7jhQzfwvivex+ev+Tyfv+bzAPzenN/jkJceMvq1+L8sZvcddm/xaCVJZTTl4Y6ICvBPwB8Aq4GbIuK7mbliqseyLew8Y2e+957vsebZNdz68K3c8vAt3PLILSx/eDn/seI/Ru+3x+w92Hf3fVm460IW7LaAhbstZOFuC9lj9h5ERAufgSSpnbVii/tQ4N7MvA8gIi4BjgWmRbhHvGSHl/Dmvd/Mm/d+8+h1T61/itseuY3lDy/n9kdv567H7+KC2y9g3YZ1o/fZoboDC3ZbwD677sOs6iwqXRUqURn3dHB4kPUD61k/uJ7+wX7WD64fvbx+YD2Dw4NUK1V6Kj1Uu6obne+p9NBT6WFm90xmVmeyfXV7ZnYXp3WXf/34r+m/t3/0MSPLqF/Odt3bsV1lu9HT7q7uTf7ykZkMDg+yYWgDA8MDDA4P0hVdVLuqdHd1U61UqUTFX14kaRNaEe49gAfrLq8GXtuCcUy5OTPnsGyvZSzba9nodZnJw30Pc/fjd3PX43dx9xO10xsfupH1A+sZyiGGhofGPe3u6h4N74zuGaPnZ3bPZKcZO9Hd1c3A0AADwwOsH1zPM88/MxrMgaEBnh96nv7Bfp4beI7nBp5jOIfHH/idE3ueQYxGvFqpjo5hw9AGBocHG1pGd1f3aMwrXRWCICLGPe2KLrqii0pXpXYalY0ud0UXmclwDjOcwyQvnB/OYTKTiKitaxO/JI0sI8mNTgGSF/6GP6j9wjHyi8fIOAHWPbOOHe/dcaPb6x8zVv1y63VF1ybnY7wx1o91vLHVn44d23jGjnfs/evnZbzzjdjU9zoiWPvUWuY8OGeTjx2Zt7Hfn/HWP/Z513+/NmdLz2VT37uJmIpjQzz11FPM+e0Lc7k14x47jyPnx5ro86r//m3p/6nx/n8ZGdvmvq8jy97c+U0915HL2/dvz9KlSyf03CarJa9xNyIiTgVOBZg7dy69vb0NP7avr29C928HQbAv+7LvjH1hHrWvKZSZDOYgzw8/z/NDz9dOh5/n6WefpmdGD4PDgwxkbQt5MGtfA8MDDOYgG4aLXwayFuiB4YHadVm7vTu6qUZtS7q7a+PzlaiMBnRkuUM5xOBw7XQohxhiCLL4QS3+2+hyJsPUAjzE0AuRLq4bzuEXfpgj6KJr9JSozf1IzIdyqPbY4WGGh164PMjg6Pdp5JeFkcsjp6M/7HU/6PX/yMyMmQw/NzzuPwQjvzw0+r0aZnj0/OicFEbGuNE/WnVhHxnPuP9QTfAf7rH/cCb5on8k6+eo0SiON76Ry4NDg6x/fH1D46tfN2wckvGed/38bOoXqtFlT8FeoS2NYWsNDQ2x4YkNW73OLf2/v6Vf9ho19v/tkes2GsM4PxON/LIwdplb+kV27C+Gc6sT69TWaEW4HwJeXnd5XnHdRjLzHOAcgCVLluREfpPp7e2dst98pjvnsnmcy+ZwHpvHuWyeqZzLVvw52E3AqyJir4joAd4NfLcF45AkqXSmfIs7Mwcj4n8AV1H7c7DzMnOCr6JKktSZWvIad2ZeCVzZinVLklRmHjlNkqQSMdySJJWI4ZYkqUQMtyRJJWK4JUkqEcMtSVKJGG5JkkrEcEuSVCKGW5KkEjHckiSViOGWJKlEDLckSSViuCVJKhHDLUlSiRhuSZJKJDKz1WPYooh4DHhgAg/ZDXh8Gw2n0ziXzeNcNofz2DzOZfM0ey73zMzdx7uhFOGeqIi4OTOXtHoc04Fz2TzOZXM4j83jXDbPVM6lu8olSSoRwy1JUolM13Cf0+oBTCPOZfM4l83hPDaPc9k8UzaX0/I1bkmSpqvpusUtSdK0NK3CHRFHR8TdEXFvRHyq1eMpm4g4LyLWRMQdddftEhE/ioh7itM5rRxjGUTEyyPipxGxIiLujIiPFdc7lxMUETMi4saIuL2Yy88X1+8VETcUP+vfjIieVo+1DCKiEhG3RsT3i8vO4yRFxKqI+FVE3BYRNxfXTcnP+LQJd0RUgH8C3gLsB7wnIvZr7ahK53zg6DHXfQr4cWa+CvhxcVmbNwiclpn7AYcBf1L8v+hcTtzzwBGZeTCwCDg6Ig4DvgT8XWbuDTwFfLCFYyyTjwEr6y47j1tnWWYuqvszsCn5GZ824QYOBe7NzPsycwNwCXBsi8dUKpl5LfDkmKuPBS4ozl8AHDelgyqhzHw4M28pzq+j9g/lHjiXE5Y1fcXFavGVwBHAZcX1zmUDImIe8Dbg3OJy4Dw225T8jE+ncO8BPFh3eXVxnbbO3Mx8uDj/CDC3lYMpm4iYDywGbsC5nJRi9+5twBrgR8BvgLWZOVjcxZ/1xvw98AlguLi8K87j1kjg6ohYHhGnFtdNyc9497ZYqKanzMyI8M8QGhQRs4DLgf+Zmc/UNnBqnMvGZeYQsCgidgauABa2eEilExHHAGsyc3lELG31eKaJ12fmQxHxEuBHEXFX/Y3b8md8Om1xPwS8vO7yvOI6bZ1HI+KlAMXpmhaPpxQiokot2hdl5reKq53LrZCZa4GfAq8Ddo6IkQ0Pf9a37HDg7RGxitrLiEcAX8F5nLTMfKg4XUPtF8pDmaKf8ekU7puAVxXvkuwB3g18t8Vjmg6+C3ygOP8B4DstHEspFK8dfgNYmZl/W3eTczlBEbF7saVNRMwE/oDaewZ+CpxQ3M253ILMPD0z52XmfGr/Nv4kM0/EeZyUiNghImaPnAeOAu5gin7Gp9UBWCLirdRex6kA52XmmS0eUqlExMXAUmqfcvMo8Fng28ClwCuofULbOzNz7BvYVCciXg9cB/yKF15P/Atqr3M7lxMQEQdRe5NPhdqGxqWZ+YWIeCW1LcddgFuBkzLz+daNtDyKXeV/lpnHOI+TU8zbFcXFbuDfM/PMiNiVKfgZn1bhliRpuptOu8olSZr2DLckSSViuCVJKhHDLUlSiRhuSZJKxHBL00hE/GdxOj8i3tvkZf/FeOuSNLX8czBpGqr/W90JPKa77rjV493el5mzmjE+SZPnFrc0jUTEyCdpnQ28ofis4I8XH9Tx5Yi4KSJ+GREfLu6/NCKui4jvAiuK675dfHDCnSMfnhARZwMzi+VdVL+uqPlyRNxRfD7xu+qW3RsRl0XEXRFxUdQfsF3SpPghI9L09CnqtriLAD+dma+JiO2A6yPi6uK+hwAHZOb9xeVTMvPJ4hCjN0XE5Zn5qYj4H5m5aJx1/RG1z8o+mNpR926KiGuL2xYD+wO/A66ndszsnzX/6Uqdwy1uqTMcBby/+HjMG6h9pOOritturIs2wEcj4nbgF9Q+uOdVbN7rgYszcygzHwWuAV5Tt+zVmTkM3AbMb8qzkTqYW9xSZwjgI5l51UZX1l4Lf3bM5SOB12XmcxHRC8zYivXWH/d6CP/NkbaaW9zS9LQOmF13+SrgvxcfN0pE7FN8qtFYOwFPFdFeCBxWd9vAyOPHuA54V/E6+u7AG4Ebm/IsJL2Iv/1K09MvgaFil/f51D57eT5wS/EGsceA48Z53A+BP46IlcDd1HaXjzgH+GVE3FJ8JOSIK6h9RvbtQAKfyMxHivBLajL/HEySpBJxV7kkSSViuCVJKhHDLUlSiRhuSZJKxHBLklQihluSpBIx3JIklYjhliSpRP4PZu5wCQZoS7UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UheoJv6s7E0"
      },
      "source": [
        "## Improve policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyLo7KEIrYDo"
      },
      "source": [
        "def improvePolicy(policy, Q, state, eps=0.1):\n",
        "    q = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "    for a in [0, 1, 2, 3, 4, 5, 6, 7]:\n",
        "        if a in Q[state].keys():\n",
        "            q[a] = Q[state][a]\n",
        "\n",
        "    policy[state] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "    idx = 0\n",
        "    _, indices = torch.sort(q, descending=True)\n",
        "    policy[state][indices[idx]] = (1.0-eps)\n",
        "    # if indices[0]==7:\n",
        "        # print(\"state =\", state)\n",
        "        # print(\"policy =\", policy[state])\n",
        "    while ((state+1)%8 == 0 and indices[idx]==0) or (((state+1)%8 == 0 or (state>=56 and state<=63)) and indices[idx]==1) or (state>=56 and state<=63 and indices[idx]==2) or (((state>=56 and state<=63) or state%8 == 0) and indices[idx]==3) or (state%8 == 0 and indices[idx]==4) or ((state%8 == 0 or (state>=0 and state<=7)) and indices[idx]==5) or (state>=0 and state<=7 and indices[idx]==6) or (((state+1)%8 == 0 or (state>=0 and state<=7)) and indices[idx]==7):\n",
        "        policy[state][indices[idx]] = 0.0\n",
        "        idx += 1\n",
        "        policy[state][indices[idx]] = (1.0-eps)\n",
        "\n",
        "    num_actions = 8.0\n",
        "    if (state+1)%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if (state+1)%8 == 0 or (state>=56 and state<=63):\n",
        "        num_actions -= 1.0\n",
        "    if state>=56 and state<=63:\n",
        "        num_actions -= 1.0\n",
        "    if (state>=56 and state<=63) or state%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state%8 == 0 or (state>=0 and state<=7):\n",
        "        num_actions -= 1.0\n",
        "    if state>=0 and state<=7:\n",
        "        num_actions -= 1.0\n",
        "    if (state+1)%8 == 0 or (state>=0 and state<=7):\n",
        "        num_actions -= 1.0\n",
        "\n",
        "    if not ((state+1)%8 == 0):\n",
        "        policy[state][0] += (eps/num_actions)\n",
        "    if not ((state+1)%8 == 0 or (state>=56 and state<=63)):\n",
        "        policy[state][1] += (eps/num_actions)\n",
        "    if not (state>=56 and state<=63):\n",
        "        policy[state][2] += (eps/num_actions)\n",
        "    if not ((state>=56 and state<=63) or state%8 == 0):\n",
        "        policy[state][3] += (eps/num_actions)\n",
        "    if state%8 != 0:\n",
        "        policy[state][4] += (eps/num_actions)\n",
        "    if not (state%8 == 0 or (state>=0 and state<=7)):\n",
        "        policy[state][5] += (eps/num_actions)\n",
        "    if not (state>=0 and state<=7):\n",
        "        policy[state][6] += (eps/num_actions)\n",
        "    if not ((state+1)%8 == 0 or (state>=0 and state<=7)):\n",
        "        policy[state][7] += (eps/num_actions)    \n",
        "    return policy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P5N73szrYLY"
      },
      "source": [
        "def walk_freq():\n",
        "    freq = {}\n",
        "    for i in range(1000):\n",
        "        for start in range(NUM_NODES):\n",
        "            states, a, r = sample_episode(8, start)\n",
        "            for s in states:\n",
        "                if s not in freq.keys():\n",
        "                    freq[s] = 0\n",
        "                freq[s] += 1\n",
        "    matrix = np.array([[int(freq[to_node_idx(i,j)]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"green\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ImEZzhAGs3eM",
        "outputId": "af751f30-6ec8-4b04-c903-93dba4757166"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRSMeU2ms2x_"
      },
      "source": [
        "model1.eval()\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Asl5Pz8WrX-S",
        "outputId": "144d206d-1b1e-41b9-e678-fedbfaa13601"
      },
      "source": [
        "cum_reward = []\n",
        "epsilon = 0.98\n",
        "model2.eval()\n",
        "NUM_ITER = 300\n",
        "WALK_LEN = 10\n",
        "walk_freq()\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    \n",
        "    for i in range(NUM_NODES):\n",
        "        G = 0.0\n",
        "        for j in reversed(range(WALK_LEN-1)):\n",
        "            G += float(imm_rewards[states[i][j+1]])\n",
        "            s = states[i][j]\n",
        "            a = actions[i][j]\n",
        "            vis_prev = False\n",
        "            for k in range(j):\n",
        "                if states[i][k]==s and actions[i][k]==a:\n",
        "                    vis_prev = True\n",
        "                    break\n",
        "            if vis_prev:\n",
        "                continue\n",
        "            \n",
        "            RET[s][a].append(G)\n",
        "            Q[s][a] = np.mean(RET[s][a])\n",
        "            POLICY = improvePolicy(POLICY, Q, s, epsilon)\n",
        "\n",
        "    epsilon *= 0.98\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    \n",
        "    cm = float(torch.sum(rewards))/float(NUM_NODES)\n",
        "    # if len(cum_reward)>0:\n",
        "        # cm = (GAMMA*cm) + ((1-GAMMA)*cum_reward[-1])\n",
        "    cum_reward.append(cm)\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  cum_reward : \"+str(cum_reward[-1]))\n",
        "    if iter in [5, 10, 20, 30, 50, 75, 150, 300]:\n",
        "        walk_freq()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >4415</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >6926</td>\n",
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              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >6549</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >6401</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6392</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >6791</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >4530</td>\n",
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              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >6846</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >10284</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9668</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >9459</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >9404</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >9643</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >10062</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >6822</td>\n",
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              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >6586</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >9626</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9235</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >8995</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8994</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >9022</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >9501</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >6532</td>\n",
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              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >6294</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >9238</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8706</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >8629</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >8797</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >9029</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >9413</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >6382</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6409</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9061</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >8788</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >8525</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >8709</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8870</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >9465</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >6294</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6654</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >9628</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >9100</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8882</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8969</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9181</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >9571</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6651</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >7068</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >10317</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >9632</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >9447</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >9431</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >9623</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >10122</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6951</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >4640</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >7036</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >6509</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >6393</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >6452</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >6471</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >6798</td>\n",
              "                        <td id=\"T_4c9db2b4_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >4494</td>\n",
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            "iter : 4 |  cum_reward : 0.203125\n",
            "iter : 5 |  cum_reward : 0.25\n"
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              "            background-color:  #aed5ae;\n",
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              "                <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >4201</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >7819</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6253</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >5441</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >5374</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6631</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >6286</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >4541</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >6265</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >9317</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9333</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >7934</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >8016</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >8256</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >10249</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >6627</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >5757</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >10348</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >8094</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >9535</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >7990</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >7643</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >9878</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >8771</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >5921</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >8583</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8890</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >7420</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >7330</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >7636</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >8564</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5993</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6412</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >10627</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >8938</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >7768</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >8197</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8606</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >8965</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >5744</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6379</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >11012</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >10086</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >9574</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >9639</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9186</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >10699</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >5660</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >6832</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >11566</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >15524</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >10757</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >9247</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >10573</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >9178</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6102</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_5715713c_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >4998</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >7670</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >7913</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >8297</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >7623</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >6220</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >7035</td>\n",
              "                        <td id=\"T_5715713c_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >4077</td>\n",
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              "    </tbody></table>"
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            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda282b6410>"
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        {
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          "name": "stdout",
          "text": [
            "iter : 6 |  cum_reward : 0.40625\n",
            "iter : 7 |  cum_reward : 0.515625\n",
            "iter : 8 |  cum_reward : 0.34375\n",
            "iter : 9 |  cum_reward : 0.34375\n",
            "iter : 10 |  cum_reward : 0.4375\n"
          ]
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        {
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              "            background-color:  #beddbe;\n",
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              "                <tr>\n",
              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >3186</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >5499</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6118</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >4398</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >4357</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5488</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >5647</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >4682</td>\n",
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              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >5135</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >7374</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >7472</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >7602</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >6493</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >6883</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >11632</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >6230</td>\n",
              "            </tr>\n",
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              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >5193</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >11429</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >7476</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >8961</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >6360</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >7820</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >7684</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >10015</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >5972</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >11027</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >9430</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >7307</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >9005</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >6488</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >7274</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5138</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6927</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >11759</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >10394</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >9636</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >9584</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >7228</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >7913</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >4680</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >5936</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >13993</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >11485</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >11077</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >12228</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >8897</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >9578</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >4833</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >6090</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >10959</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >23238</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >12494</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >9197</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >11001</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >7781</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >5248</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >4012</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >8490</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >7651</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >9976</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >8765</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >5800</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >6947</td>\n",
              "                        <td id=\"T_623a37d2_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >3428</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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              "<pandas.io.formats.style.Styler at 0x7fda28148ed0>"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 11 |  cum_reward : 0.53125\n",
            "iter : 12 |  cum_reward : 0.546875\n",
            "iter : 13 |  cum_reward : 0.625\n",
            "iter : 14 |  cum_reward : 0.53125\n",
            "iter : 15 |  cum_reward : 0.4375\n",
            "iter : 16 |  cum_reward : 0.5625\n",
            "iter : 17 |  cum_reward : 0.671875\n",
            "iter : 18 |  cum_reward : 0.875\n",
            "iter : 19 |  cum_reward : 1.015625\n",
            "iter : 20 |  cum_reward : 0.84375\n"
          ]
        },
        {
          "output_type": "display_data",
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              "            background-color:  #d9ead9;\n",
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              "            background-color:  #cee5ce;\n",
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              "            background-color:  #cfe5cf;\n",
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              "            background-color:  #bddcbd;\n",
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              "            background-color:  #e2efe2;\n",
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              "            background-color:  #b6d9b6;\n",
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              "            background-color:  #d4e8d4;\n",
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              "            background-color:  #beddbe;\n",
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              "            background-color:  #d0e6d0;\n",
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              "            background-color:  #ddecdd;\n",
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              "            background-color:  #b7dab7;\n",
              "            color:  #000000;\n",
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              "            background-color:  #dfeddf;\n",
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              "            background-color:  #c0dec0;\n",
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              "            background-color:  #bfdebf;\n",
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              "            background-color:  #dbebdb;\n",
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              "            background-color:  #a7d2a7;\n",
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              "            background-color:  #c5e0c5;\n",
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              "            background-color:  #bcdcbc;\n",
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              "            background-color:  #e6f1e6;\n",
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              "            background-color:  #97ca97;\n",
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              "            background-color:  #a4d0a4;\n",
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              "            background-color:  #d1e6d1;\n",
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              "            background-color:  #aed5ae;\n",
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              "            background-color:  #008000;\n",
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              "            background-color:  #c4e0c4;\n",
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              "            background-color:  #c8e2c8;\n",
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              "            background-color:  #c7e1c7;\n",
              "            color:  #000000;\n",
              "        }#T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col1{\n",
              "            background-color:  #78bb78;\n",
              "            color:  #000000;\n",
              "        }#T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col2{\n",
              "            background-color:  #c2dfc2;\n",
              "            color:  #000000;\n",
              "        }#T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col3{\n",
              "            background-color:  #a8d2a8;\n",
              "            color:  #000000;\n",
              "        }#T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col5{\n",
              "            background-color:  #c3e0c3;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2530</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >4304</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >5847</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >3691</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >3720</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6128</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >4150</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >2804</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >3889</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >5529</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >5784</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >7236</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >7178</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >5436</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >10239</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >4695</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >4014</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >11243</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >6183</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >9801</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >6974</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >4890</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >5746</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >11024</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >4626</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >9508</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >9638</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >9822</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >6949</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >4942</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >5240</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >3996</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >5001</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >13575</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >7512</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >8841</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >10342</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >5151</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >6241</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >3390</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >5181</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >16317</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >10108</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >14297</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >10275</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >7304</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >5195</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >4496</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >6764</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >12643</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >41228</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >13668</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >8886</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >8425</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >8489</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >3766</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >4826</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >21538</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >9323</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >13438</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >11091</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >9067</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >5175</td>\n",
              "                        <td id=\"T_6e6c0184_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >2691</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda2823ff50>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 21 |  cum_reward : 0.78125\n",
            "iter : 22 |  cum_reward : 0.9375\n",
            "iter : 23 |  cum_reward : 1.125\n",
            "iter : 24 |  cum_reward : 1.234375\n",
            "iter : 25 |  cum_reward : 0.859375\n",
            "iter : 26 |  cum_reward : 1.046875\n",
            "iter : 27 |  cum_reward : 1.484375\n",
            "iter : 28 |  cum_reward : 1.21875\n",
            "iter : 29 |  cum_reward : 1.34375\n",
            "iter : 30 |  cum_reward : 1.234375\n"
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              "                <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2030</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >3557</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >2771</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >2657</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >2729</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >3960</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >2941</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >2169</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >3243</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >5747</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >4200</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >6263</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >6897</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >3878</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >7324</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >4296</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >5731</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >9721</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >5007</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >9989</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >6387</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >4065</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >5872</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >7383</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >3909</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >8993</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >5499</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >13703</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >6988</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >6616</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >4137</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >3014</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >4073</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >12459</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >6399</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >8699</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >11590</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >7552</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >5642</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >2589</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >4258</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >16909</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >9239</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >16622</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >12457</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >7588</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >6399</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >2710</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >4552</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >12199</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >58140</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >17035</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >11476</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >8495</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >5821</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >2888</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >3093</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >11126</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >29549</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >17260</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >8984</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >8132</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >4284</td>\n",
              "                        <td id=\"T_7a8c1d28_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >2105</td>\n",
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              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda281d3f50>"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 31 |  cum_reward : 1.484375\n",
            "iter : 32 |  cum_reward : 1.453125\n",
            "iter : 33 |  cum_reward : 1.390625\n",
            "iter : 34 |  cum_reward : 1.125\n",
            "iter : 35 |  cum_reward : 1.359375\n",
            "iter : 36 |  cum_reward : 1.71875\n",
            "iter : 37 |  cum_reward : 1.640625\n",
            "iter : 38 |  cum_reward : 1.515625\n",
            "iter : 39 |  cum_reward : 1.625\n",
            "iter : 40 |  cum_reward : 1.640625\n",
            "iter : 41 |  cum_reward : 1.5625\n",
            "iter : 42 |  cum_reward : 1.71875\n",
            "iter : 43 |  cum_reward : 1.84375\n",
            "iter : 44 |  cum_reward : 1.875\n",
            "iter : 45 |  cum_reward : 2.109375\n",
            "iter : 46 |  cum_reward : 1.8125\n",
            "iter : 47 |  cum_reward : 1.859375\n",
            "iter : 48 |  cum_reward : 1.734375\n",
            "iter : 49 |  cum_reward : 2.046875\n",
            "iter : 50 |  cum_reward : 2.0625\n"
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              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1453</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1733</td>\n",
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              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >2051</td>\n",
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              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >4394</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >2954</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1480</td>\n",
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              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >2026</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >4377</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >2608</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >4142</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >7878</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >4802</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >3577</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1877</td>\n",
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              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >4790</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >4002</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >3499</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >12133</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >6535</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >2816</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >4445</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1744</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2619</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >8476</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >4351</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >16790</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >5764</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >5443</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >2389</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1751</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >3067</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >12660</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >5392</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >17302</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >4646</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >6184</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >3888</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1741</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >3180</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >20233</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8068</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >19429</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >11529</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >5643</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >3620</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >1846</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >3219</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >11002</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >87897</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >17362</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >9289</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >6020</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >5383</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >1964</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2046</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >8962</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >50851</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >19099</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >9117</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >8177</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >3376</td>\n",
              "                        <td id=\"T_868ae0b4_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1651</td>\n",
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            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda28273c10>"
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          },
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 51 |  cum_reward : 2.03125\n",
            "iter : 52 |  cum_reward : 2.140625\n",
            "iter : 53 |  cum_reward : 1.984375\n",
            "iter : 54 |  cum_reward : 2.125\n",
            "iter : 55 |  cum_reward : 2.015625\n",
            "iter : 56 |  cum_reward : 2.21875\n",
            "iter : 57 |  cum_reward : 2.25\n",
            "iter : 58 |  cum_reward : 2.171875\n",
            "iter : 59 |  cum_reward : 2.203125\n",
            "iter : 60 |  cum_reward : 2.421875\n",
            "iter : 61 |  cum_reward : 2.53125\n",
            "iter : 62 |  cum_reward : 2.40625\n",
            "iter : 63 |  cum_reward : 2.5\n",
            "iter : 64 |  cum_reward : 2.421875\n",
            "iter : 65 |  cum_reward : 2.34375\n",
            "iter : 66 |  cum_reward : 2.4375\n",
            "iter : 67 |  cum_reward : 2.46875\n",
            "iter : 68 |  cum_reward : 2.46875\n",
            "iter : 69 |  cum_reward : 2.421875\n",
            "iter : 70 |  cum_reward : 2.59375\n",
            "iter : 71 |  cum_reward : 2.484375\n",
            "iter : 72 |  cum_reward : 2.53125\n",
            "iter : 73 |  cum_reward : 2.421875\n",
            "iter : 74 |  cum_reward : 2.390625\n",
            "iter : 75 |  cum_reward : 2.515625\n"
          ]
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              "                <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1208</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1348</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >1375</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1537</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >2778</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >3735</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >2461</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1252</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1415</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >3605</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >1906</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >3118</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >7149</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >3908</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >2796</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1468</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1444</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >2770</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >5161</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >11714</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >5531</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1942</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >3768</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1382</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >1616</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >5082</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >3017</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >19556</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >4381</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >4783</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1668</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1337</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1952</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9500</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >3593</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >20853</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >3401</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >5637</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >3030</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1292</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >2077</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >16983</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >5913</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >22704</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >10773</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >4506</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >2795</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >1389</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >2127</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >8512</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >113630</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >16142</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >7427</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >4704</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >4166</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >1467</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_93562344_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1559</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >7321</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >74140</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >18582</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >8345</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >7218</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >2737</td>\n",
              "                        <td id=\"T_93562344_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1314</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda30d0a350>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 76 |  cum_reward : 2.578125\n",
            "iter : 77 |  cum_reward : 2.640625\n",
            "iter : 78 |  cum_reward : 2.546875\n",
            "iter : 79 |  cum_reward : 2.5625\n",
            "iter : 80 |  cum_reward : 2.578125\n",
            "iter : 81 |  cum_reward : 2.546875\n",
            "iter : 82 |  cum_reward : 2.78125\n",
            "iter : 83 |  cum_reward : 2.578125\n",
            "iter : 84 |  cum_reward : 2.625\n",
            "iter : 85 |  cum_reward : 2.8125\n",
            "iter : 86 |  cum_reward : 2.875\n",
            "iter : 87 |  cum_reward : 2.65625\n",
            "iter : 88 |  cum_reward : 2.75\n",
            "iter : 89 |  cum_reward : 2.875\n",
            "iter : 90 |  cum_reward : 2.8125\n",
            "iter : 91 |  cum_reward : 2.78125\n",
            "iter : 92 |  cum_reward : 3.03125\n",
            "iter : 93 |  cum_reward : 2.765625\n",
            "iter : 94 |  cum_reward : 2.875\n",
            "iter : 95 |  cum_reward : 2.734375\n",
            "iter : 96 |  cum_reward : 2.890625\n",
            "iter : 97 |  cum_reward : 2.90625\n",
            "iter : 98 |  cum_reward : 2.75\n",
            "iter : 99 |  cum_reward : 2.984375\n",
            "iter : 100 |  cum_reward : 2.8125\n",
            "iter : 101 |  cum_reward : 2.90625\n",
            "iter : 102 |  cum_reward : 3.0\n",
            "iter : 103 |  cum_reward : 2.828125\n",
            "iter : 104 |  cum_reward : 3.015625\n",
            "iter : 105 |  cum_reward : 3.078125\n",
            "iter : 106 |  cum_reward : 3.015625\n",
            "iter : 107 |  cum_reward : 2.984375\n",
            "iter : 108 |  cum_reward : 2.984375\n",
            "iter : 109 |  cum_reward : 2.953125\n",
            "iter : 110 |  cum_reward : 3.015625\n",
            "iter : 111 |  cum_reward : 3.046875\n",
            "iter : 112 |  cum_reward : 3.109375\n",
            "iter : 113 |  cum_reward : 3.09375\n",
            "iter : 114 |  cum_reward : 3.078125\n",
            "iter : 115 |  cum_reward : 3.078125\n",
            "iter : 116 |  cum_reward : 2.96875\n",
            "iter : 117 |  cum_reward : 3.171875\n",
            "iter : 118 |  cum_reward : 3.125\n",
            "iter : 119 |  cum_reward : 3.109375\n",
            "iter : 120 |  cum_reward : 3.140625\n",
            "iter : 121 |  cum_reward : 3.0\n",
            "iter : 122 |  cum_reward : 3.234375\n",
            "iter : 123 |  cum_reward : 3.09375\n",
            "iter : 124 |  cum_reward : 3.140625\n",
            "iter : 125 |  cum_reward : 3.03125\n",
            "iter : 126 |  cum_reward : 3.109375\n",
            "iter : 127 |  cum_reward : 3.1875\n",
            "iter : 128 |  cum_reward : 3.0\n",
            "iter : 129 |  cum_reward : 3.09375\n",
            "iter : 130 |  cum_reward : 3.1875\n",
            "iter : 131 |  cum_reward : 3.109375\n",
            "iter : 132 |  cum_reward : 3.203125\n",
            "iter : 133 |  cum_reward : 3.1875\n",
            "iter : 134 |  cum_reward : 3.28125\n",
            "iter : 135 |  cum_reward : 3.109375\n",
            "iter : 136 |  cum_reward : 3.140625\n",
            "iter : 137 |  cum_reward : 3.21875\n",
            "iter : 138 |  cum_reward : 3.203125\n",
            "iter : 139 |  cum_reward : 3.15625\n",
            "iter : 140 |  cum_reward : 3.234375\n",
            "iter : 141 |  cum_reward : 3.125\n",
            "iter : 142 |  cum_reward : 3.265625\n",
            "iter : 143 |  cum_reward : 3.265625\n",
            "iter : 144 |  cum_reward : 3.203125\n",
            "iter : 145 |  cum_reward : 3.25\n",
            "iter : 146 |  cum_reward : 3.171875\n",
            "iter : 147 |  cum_reward : 3.15625\n",
            "iter : 148 |  cum_reward : 3.171875\n",
            "iter : 149 |  cum_reward : 3.25\n",
            "iter : 150 |  cum_reward : 3.171875\n"
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              "                <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1039</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1055</td>\n",
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              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >3170</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >2083</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1045</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1067</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >3097</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >1167</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >2171</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >6269</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >3163</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >2168</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1082</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1079</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >2148</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >4230</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10382</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >4286</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1180</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >3125</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1065</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >1097</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >4199</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >1425</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >19193</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >2528</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >4077</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1147</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1052</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1159</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >7550</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >1602</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >21877</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1555</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >4994</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >2206</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1059</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1164</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >12667</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >2199</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >23909</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >9342</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >3298</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >2133</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >1061</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >1144</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >3737</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >144628</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >11702</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >4717</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >3298</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >3219</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >1087</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1065</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >3463</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >110269</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >14065</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >7278</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >6273</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >2147</td>\n",
              "                        <td id=\"T_a308ff00_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1055</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda30c3ded0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 151 |  cum_reward : 3.328125\n",
            "iter : 152 |  cum_reward : 3.21875\n",
            "iter : 153 |  cum_reward : 3.234375\n",
            "iter : 154 |  cum_reward : 3.296875\n",
            "iter : 155 |  cum_reward : 3.28125\n",
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          ]
        },
        {
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              "                <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1004</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1004</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col2\" class=\"data row0 col2\" >1002</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1003</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col4\" class=\"data row0 col4\" >2011</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col5\" class=\"data row0 col5\" >3005</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col6\" class=\"data row0 col6\" >2002</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1002</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1001</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col1\" class=\"data row1 col1\" >3009</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col2\" class=\"data row1 col2\" >1010</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col3\" class=\"data row1 col3\" >2006</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col4\" class=\"data row1 col4\" >6015</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col5\" class=\"data row1 col5\" >3005</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col6\" class=\"data row1 col6\" >2004</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1006</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1002</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col1\" class=\"data row2 col1\" >2001</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col2\" class=\"data row2 col2\" >4015</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10024</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col4\" class=\"data row2 col4\" >4019</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1003</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col6\" class=\"data row2 col6\" >3012</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1002</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col0\" class=\"data row3 col0\" >1004</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col1\" class=\"data row3 col1\" >4004</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col2\" class=\"data row3 col2\" >1015</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col3\" class=\"data row3 col3\" >19033</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col4\" class=\"data row3 col4\" >2019</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col5\" class=\"data row3 col5\" >4006</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1012</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1005</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1007</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col1\" class=\"data row4 col1\" >7010</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col2\" class=\"data row4 col2\" >1031</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col3\" class=\"data row4 col3\" >22013</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1029</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col5\" class=\"data row4 col5\" >5006</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col6\" class=\"data row4 col6\" >2017</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1005</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1011</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col1\" class=\"data row5 col1\" >11076</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col2\" class=\"data row5 col2\" >1046</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col3\" class=\"data row5 col3\" >24006</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col4\" class=\"data row5 col4\" >9032</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col5\" class=\"data row5 col5\" >3019</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col6\" class=\"data row5 col6\" >2010</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row5_col7\" class=\"data row5 col7\" >1002</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col0\" class=\"data row6 col0\" >1009</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col1\" class=\"data row6 col1\" >2093</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col2\" class=\"data row6 col2\" >152569</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col3\" class=\"data row6 col3\" >10092</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col4\" class=\"data row6 col4\" >4040</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col5\" class=\"data row6 col5\" >3019</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col6\" class=\"data row6 col6\" >3000</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row6_col7\" class=\"data row6 col7\" >1004</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1001</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col1\" class=\"data row7 col1\" >2092</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col2\" class=\"data row7 col2\" >121403</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col3\" class=\"data row7 col3\" >12087</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col4\" class=\"data row7 col4\" >7001</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col5\" class=\"data row7 col5\" >6000</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col6\" class=\"data row7 col6\" >2003</td>\n",
              "                        <td id=\"T_bbd1ec68_447c_11ec_8320_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1002</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fda28230d90>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2FN_u_3tnAp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "37080204-08e3-46bb-ac14-5a9e9eaaa5a3"
      },
      "source": [
        "x = [i for i in range(1,len(cum_reward)+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, cum_reward, 'r', label='Average Cumulative Reward')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('reward')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Average Cumulative Reward per episode vs Iterations\")\n",
        "fig.savefig('CumReward_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5Xk_sditm95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "6d9b8a54-7f5d-48b2-fc5e-293b10ad4677"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZjsfBUNtm7S"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}