QLearning tic-tac-toe Agent

In this project, we create a qlearning based tic-tac-toe agent to learn and play against pseudo logical AI, random agent and human players. The file ttt_class.py contains the code to create the Q Table for future references by playing against a pseudo logical agent. In the file ttt_performance_measure.py, we can visualize the performance of our Q learning agent against a random agent or the pseudo logical agent using bar chart plots. In the file ttt_human_vs_ai.py, we have a very simplistic GUI based tictactoe game where we can play against the Q learning agent. The game is designed to keep count of the results of each game and restarts automatically once a game finishes. We can simply close the game by clicking the X button on the window to close the game.

This project is essentially for educational and experimental purposes to see how reinforcement learning(specifically, QLearning) works in the classic game of tic tac toe.

Installation:

$ git clone https://github.com/alishaz-polymath/tic-tac-toe.git

Usage:

1. Run the ttt_class.py file to generate a newer version of Qlearn_new.pickle file, if you wish, else go to step 2.
2. Run the ttt_performance_measure.py file to visualise the performance index between various agents, which can be selected from within the file inside the main() definition.
3. Run the ttt_human_vs_ai.py file to play against the Q Learning agent in with a simplistic GUI.

Essential Libraries:

• numpy
• random
• matplotlib
• pickle
• pygame

Example Performance Measure Graphs:

Example Game Board:

License:

This project is licensed under the MIT License.