This repo teaches an agent to play tic-tac-toe using the standard q-learning algorithm. Users can play against another human or the trained agent. When a human plays against the agent, the agent uses imitation learning and uses the game history and outcome to update its policy.

This repo contains the following folders and files:

• agents : Trained agents

  • Q_learning_TicTacToe_policy: Trained agent policy (state-value pairs) stored as a pickle object.

• images : Images used in this Readme file

  • tictactoe_screenshot.png: Screen shot from testing the agent.

• game.py - Main script for the tic-tac-toe game class and all of its methods. Running this file as main allows the user to play against another human.

• play_agent.py - This script allows the user to play the agent. When run as main, the user can select X or O and then the game begins with a random toss of who goes first: the human or agent.

• Q_learning_agent.py - Main script for the Q-Learning-Agent class. When run as main, the agent plays 500,000 games against itself while updating its policy using the standard q-learning algorithm.

• settings.py - Script that includes all the user settings for the tic-tac-toe game, q-learning agent, and training process.

1. Sutton, Richard S. and Barto, Andrew G. Reinforcement Learning: An Introduction, 2nd ed. The MIT Press; Cambridge, Massachusetts. 2018