QLAgentExample_mini.nlogo

extensions [qlearningextension]

globals [wall-size q-learning_trial_count]
breed [sources source]
breed [bugs bug]
bugs-own [xcor-init ycor-init heading-init sns_left sns_right reward-list vsum vsum-dir vsum-dist slitherometer forward-movement]
patches-own [odor source-color reward endstate?]

to setup
  set wall-size round max-pxcor / 10
  clear-all
  create-sources 1 [
    set shape "circle"
    set size 2
    set color yellow
    setxy (max-pxcor - 2) (2 - max-pycor)
  ]
  create-sources 1 [
    set shape "circle"
    set size 2
    set color 95
    setxy 0 2
    ;setxy (max-pxcor - 2) (max-pycor - 2)
    ;setxy (max-pxcor - 4) (max-pycor - 3)
  ]
  set-current-plot "Ave Reward Per Episode"
  set-plot-y-range -10 10
  set-patch-rewards
  create-bugs 1 [
    set xcor-init 2 - max-pxcor
    set ycor-init max-pycor - 1
    set heading-init 90
    setxy xcor-init ycor-init
   set heading heading-init
    set color 22
    pen-down
  ]
  ask bugs[
    set vsum [0 0]
    qlearningextension:state-def-extra ["vsum-dir" "vsum-dist" "sns_left" "sns_right"] [bla] ;"sns_left" "sns_right" "vsum-dir" "vsum-dist"
    (qlearningextension:actions [goLeft] [goRight])  ;[goLeft] [goRight] [goLeftB] [goRightB]
    qlearningextension:reward [rewardFunc]
    qlearningextension:end-episode [isEndState] resetEpisode
    qlearningextension:action-selection "e-greedy" [0.5 0.08]
    qlearningextension:learning-rate 1
    qlearningextension:discount-factor 0.75

    ; used to create the plot
    create-temporary-plot-pen (word who)
    set-plot-pen-color color
    set reward-list []
  ]

;  repeat 10 [
;    diffuse odor 1
;    ask patches [set pcolor scale-color yellow odor 0 1]
;  ]
end

to go
  ask sources [set odor 1 set source-color color]
  diffuse odor 1
  recolor-patches
  ask bugs [
    update-sensors
    update-state-inputs
    if Enable-Path-Integration [calculate-path-integration]
    qlearningextension:learning
    ;print(qlearningextension:get-qtable)
  ]
end

to recolor-patches
  ask patches [
    ifelse show-Q-rewards [
      set pcolor scale-color red reward -50 20
    ][
      set odor 0.9 * odor set pcolor scale-color white odor 0 1
      if (abs pxcor >= max-pxcor - wall-size) or (abs pycor >= max-pycor - wall-size) [set pcolor 125]
    ]
  ]
end

to update-state-inputs
;  set discretized-sns-odors []
;  let indices range(length sns_odors_left)
;  (foreach indices sns_odors_left sns_odors_right [[n sol sor] ->
;    set discretized-sns-odors (sentence discretized-sns-odors (discretize-senses sol sor))
;    ])
;  set slitherometer_Q precision slitherometer 0
;  set heading_Q precision heading 0
  set vsum-dir precision (item 0 vsum) 0
  set vsum-dist precision (item 1 vsum) 0
;  set-variables q-learning-sns-list discretized-sns-odors
end

to update-sensors
  if abs xcor < max-pxcor and abs ycor < max-pycor [
    let odor_left [odor] of patch-left-and-ahead 40 (0.4 * size)
    ifelse odor_left > 1e-7 [set sns_left 1] [set sns_left 0]

    let odor_right [odor] of patch-right-and-ahead 40 (0.4 * size)
    ifelse odor_right > 1e-7 [set sns_right 1] [set sns_right 0]

    if odor_left > odor_right and sns_right > 0 [set sns_left sns_left + 1]
    if odor_right > odor_left and sns_left > 0 [set sns_right sns_right + 1]
  ]
end

to goRight
  set heading heading + 45
  fd 1
end

to goLeft
  set heading heading - 45
  fd 1
end

to goRightB
  set heading heading + 90
  fd 1
end

to goLeftB
  set heading heading - 90
  fd 1
end


;to goUp
;    set heading 0
;    fd 1
;end
;
;to goDown
;    set heading 180
;    fd 1
;end
;
;to goLeft
;    set heading 270
;    fd 1
;end
;
;to goRight
;    set heading 90
;    fd 1
;end


to resetPosition
  pen-up
  set slitherometer 0 set vsum [0 0]
  set heading heading-init
  setxy xcor-init ycor-init
  pen-down
end

to resetEpisode
  set q-learning_trial_count q-learning_trial_count + 1
  resetPosition
  set-current-plot "Ave Reward Per Episode"
  set-current-plot-pen (word who)
  plot mean reward-list
  set reward-list []
end

to-report bla
  report "c"
end

to set-patch-rewards
  let yellow-source one-of sources with [color = yellow]
  let blue-source one-of sources with [color = 95]
  ask patches [set pcolor black set reward -20]
  ask yellow-source [ask patches in-radius 10 [set reward -1 * (int distance  myself)]]
  ask blue-source [ask patches in-radius 4 [set reward -2 * (int distance  myself)]]
  ask yellow-source [ask patches in-radius 2 [set reward 40 set endstate? true]]
  ask patches [if (abs pxcor > max-pxcor - 2) or (abs pycor > max-pycor - 2) [set reward -100 set endstate? true]]


end

to-report rewardFunc
  set reward-list lput [reward] of patch-here reward-list
  report [reward] of patch-here
end

to-report isEndState
  if [endstate?] of patch-here = true[
    report true
  ]
  report false
end


to calculate-path-integration
  set slitherometer slitherometer + 1
  if slitherometer > 0 [
    set vsum sum-vectors ( list vsum (list heading 1))
  ]
end

to-report round-list [v p] ;Recursive function! to round all elements in a list (even nested)
  ifelse is-list? v [report (map [[vitem] -> (round-list vitem p)] v)] [report precision v p]
end

to-report sum-vectors [vector-list]; vector list in the form of [[a0 l0] [a1 l1] [a2 l2] ...] where "a" is angle, "l" is length of vector
  ifelse is-list? vector-list [
    let angles (map [[vector] -> (item 0 vector)] vector-list)
    let lengths (map [[vector] -> (item 1 vector)] vector-list)
    let x-components (map [[vector] -> ((item 1 vector) * sin (item 0 vector))] vector-list)
    let y-components (map [[vector] -> ((item 1 vector) * cos (item 0 vector))] vector-list)
    let sum-x sum x-components
    let sum-y sum y-components
    let sum-length sqrt ((sum-x ^ 2) + (sum-y ^ 2))
    let sum-angle 0
    if sum-x != 0 and sum-y != 0 [set sum-angle atan sum-x sum-y]
    report (list sum-angle sum-length)
  ][
    report [0 0]
  ]
end


to clear-trails-save-view
  export-view (word "QRUN_trial" q-learning_trial_count "_tick" ticks ".png")
  clear-drawing
end
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Path Integration: \n[direction distance]
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Color new paths in yellow
ask bugs [set color yellow]
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@#$#@#$#@
## WHAT IS IT?

This is a simple example of ϵ-greedy Q-learning in a very small environment, which is a significantly reduced version of the landmarks environment used in ASIMOV-FAM simulations. The 6x6 patch environment is enclosed by walls, and contains two landmarks that provide odor information. The yellow landmark provides the highest reward in the environment, and serves as an end-state, the blue landmark provides second-highest reward without an end-state, and the walls provide the most negative reward and also serve as an end-state.

The agent's state-space is defined by discretized sensory information it receives from the environment: left odor sensation, right odor sensation, and path-integrated distance and heading information, all of which are displayed on the right-hand side. Path integration can be turned on and off via the "Enable-Path-Integration" switch on the left-hand side.

The agent's action-space is defined simply by two actions: Go Left and Go Right, which turns the agent by 45 degrees, left and right respectively, and moves the agent forward by 1 patch. 

The current Q-learning simulations parameters are: initial ϵ = 0.5, ϵ decay = 0.08, learning rate = 0.99, and discount factor = 0.75.

The **Setup** button sets up the environment and initializes the ϵ-greedy Q-learning algorithm, while the **Step** button runs 1 simulation step of the the Q-learning trials, and **Go** button runs the simulation continuously. After Setup, the reward values of the environment can be visualized via the "show-Q-rewards".

On the left-hand side, the average reward value per trial is displayed over time, and the "Color new paths in yellow" function can be used to color all new agent trajectories in yellow, which helps in identifying if trajectories have converged.

## THINGS TO NOTICE

In this small environment, after a short time of training with Q-learning (< 700 trials), the agent's trajectories may converge to the shortest paths between its start position and the landmarks (with yellow landmark as an end-point), which maximizes reward. Without path integration information, the Q-learning trials are much less successful.
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