Q-Learning Agent for CliffWalking

Project Overview

This project implements a Q-Learning agent to solve the CliffWalking environment from OpenAI Gym. The agent is trained to navigate a grid world environment, avoiding cliffs and finding the shortest path to the goal.

Features

• Implementation of the Q-Learning algorithm.
• Epsilon-greedy strategy for action selection.
• Training and testing phases for performance evaluation.
• Ability to save and load trained Q-tables.

Requirements

• Python 3.x
• OpenAI Gym
• NumPy

Usage

1. Run the script: python3 main.py
2. Follow the prompt to load an existing Q-table or train a new agent.

Q-Learning Agent

The agent is designed to:

• Learn optimal policies via Q-Learning.
• Use an epsilon-greedy strategy for a balance between exploration and exploitation.

Training

• The agent is trained over a specified number of episodes, learning to maximize rewards in the CliffWalking environment.
• The Q-table records the value of taking certain actions in specific states.

Testing

• The agent's performance is evaluated over a number of test episodes.
• Rewards per episode are recorded to gauge the effectiveness of the learned policy.