Reinforcement Learning in ConnectX

This repository contains implementations of various Reinforcement Learning (RL) algorithms for the ConnectX game. These implementations showcase different approaches to training an AI agent to play efficiently.

Implemented Algorithms

• Proximal Policy Optimization (PPO)
• Deep Q-Learning (DQN)
• Minimax Algorithm
• Dynamic Rewards for RL Training

Each implementation provides insights into the training process and strategies for decision-making in ConnectX.

Explore, experiment, and enhance these models to improve their performance in ConnectX!

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