An example of a custom environment for https://github.com/openai/gym.
I want to try out self-play in a Reinforcement Learning context. Rather than the board game environments on openai/gym right now, which are "single-player" by providing a built-in opponent, I want to create an agent that learns a strategy by playing against itself, so it will try to maximize the reward for "player 1" and minimize it for "player 2".
The canonical example of a simple two player game is Tic Tac Toe, also known as Noughts and Crosses.
Due to the original enemy is only randomly selecting from the empty space, it's too weak to train a better AI network. I write a new class Agent, it use the minimax decision tree to calculate the next step. But I found it will always take the first best step in the same situation. It's not good for training the AI. So I randomly swap the order of the step. The result is better than before.
Add the train_tictactoe.py and enjoy_tictactoe.py two files. Those base on the deepq of OPENAI baselines. You can run that command. It will train 100,000 Steps.
python train_tictactoe.py
Then you can run enjoy_tictactoe.py. You will get about 90% win rate.
python enjoy_tictactoe.py
