This project demonstrates the use of a vanilla Q-Learning agent to learn how to balance a pole on a cart. The agent learns through trial and error, updating its Q-table based on the rewards it receives.
Install the requirements in requirements.txt
Run the main.py file. You may change the parameters such as learning rate, and training_episodes to experiment with the agent's performance.