Connect Four is a board game that was released by Howard Wexler in 1974. In a 7x6 board, a player wins when the player places tiles in any direction to form a line of length four. Under the off the shelf Connect four environment by Lucas Bertola, which provides different levels of opponent players for us to train a model, our project explores different RL algorithms and reward strategy to train an AI that plays connect four. In our project, we will try Proximal policy optimization(PPO), Deep Q-network(DQN), and Alphazero to train a powerful AI and evaluate their performances, and compare different stable baselines3 strategies(Mlp,Cnn). The AI performance will be evaluated by rew mean score, elo ranking(environment built-in system), visulization of the actual game plays, and the winning rate against trained models.

Source code: https://github.com/guangf1/Connect4

Reports:

• Proposal
• Status
• Final