This repository contains a Q-learning agent trained on the CliffWalking-v0 environment from Gymnasium. The agent learns to navigate the cliff, avoiding falling into the cliff zone while reaching the goal with minimal penalties. The Q-learning algorithm is implemented with epsilon-greedy exploration and updates the Q-table based on state-action-reward transitions.
Files:
train.py: The main script that trains the Q-learning agent.
cliffWalking_qtable.npy: The saved Q-table after training.
replay.mp4: A video of the agent's performance after training.
Training Details:
Environment: CliffWalking-v0 (Gymnasium)
Episodes: 30,000
Learning Rate (α): 0.2
Discount Factor (γ): 0.97
Epsilon (ε): 0.2 (exploration vs exploitation trade-off)
The agent starts by exploring the environment randomly and gradually learns the optimal path to avoid falling off the cliff while reaching the goal.
How to Run:
1. Install Dependencies:
Make sure you have the required packages installed:
