This repository provides Python solutions to exercises and programming problems from the "Reinforcement Learning: An Introduction" textbook (2nd Edition) by Sutton & Barto. It serves as a valuable resource for students and practitioners seeking to verify their understanding and implementation of RL concepts, offering a centralized collection of solutions that are otherwise scattered online.

How It Works

The project implements solutions to various reinforcement learning algorithms and concepts, including multi-armed bandits, Markov Decision Processes, dynamic programming, Monte Carlo methods, temporal-difference learning, n-step bootstrapping, planning, and approximate methods. Programming problems leverage the OpenAI Gym API for agent-environment interaction, ensuring compatibility with a widely-used RL simulation framework.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt (implicitly includes numpy and gym).
• Run exercises: Execute Python notebooks (e.g., exercise02-05.ipynb) or scripts.
• Prerequisites: Python 3, NumPy, OpenAI Gym.

Highlighted Details

• Solutions cover all chapters of Sutton & Barto's 2nd Edition.
• Programming problems utilize the OpenAI Gym API.
• Code is written in Python 3 with NumPy for numerical operations.
• Includes solutions for specific exercises like "Jack's Car Rental" and "Windy Gridworld."

Maintenance & Community

The repository is maintained by a single author. Community contributions are encouraged via opening issues for corrections.

Licensing & Compatibility

The repository does not explicitly state a license. The content is provided as solutions to a copyrighted textbook.

Limitations & Caveats

The author notes that solutions may contain errors and encourages community feedback. The public availability of solutions might deviate from the authors' original intent for the exercises.