Deep Q Learning (DQN) for Gymnasium's CartPole task

Model-free, off-policy, Q-learning reinforcement learning (ICL).

Implemented an agent that balances the cart pole for as long as possible.

Methods

• DQN
• Epsilon-greedy exploration policy
• DDQN (to avoid action overestimation)
• Epsilon schedule decaying
• Isolated hyperparameter tuning

Impact of different epsilon decay speeds on task performance