1. Implement the board game.
2. Design an evaluation function and implement an alpha-beta pruning player, which I will explain a couple of weeks into the semester.
3. Design, code, and train a reinforcement learning player to play the game. Reinforcement learners can take several days to a couple of weeks to train, depending on the number of errors in the code that you find along the way and the complexity of your game.
4. Compare the RL player to the alpha-beta player by playing them against each other and possibly playing them against other players you find on the web or other people. For example, if you implement checkers, you could play against hinook, which is Schaeffer's world-class player (and your player will lose).
5. Write a summary of your project, which should include: a description of the game; a brief summary of existing work (if any) on designing players for this game; an experimental comparison of the RL player and the alpha-beta player; a conclusion that should attempt to explain the experimental results.
1-2 already complete,attached with code. please do 3-5
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