This repository serves as a comprehensive educational resource for Reinforcement Learning (RL), targeting students and practitioners seeking to understand and implement core RL algorithms. It provides a structured overview of RL concepts, from foundational Markov Decision Processes to advanced techniques like Deep Q-Networks and Meta-Learning, accompanied by illustrative code examples.

How It Works

The project systematically breaks down RL into key components and algorithms, including Dynamic Programming (Policy and Value Iteration), Monte Carlo methods, and Temporal Difference learning (SARSA, Q-Learning). It then progresses to function approximation, policy-based methods (Policy Gradient, Actor-Critic), and deep RL architectures like DQN, explaining the underlying principles and mathematical formulations. The inclusion of OpenAI Gym environments allows for practical application and experimentation with these algorithms.

Quick Start & Requirements

• Install: Primarily uses Python with libraries like NumPy, TensorFlow, and OpenAI Gym. Installation typically involves pip install -r requirements.txt.
• Prerequisites: Python 3.x, TensorFlow, OpenAI Gym. Some demos might require specific versions or additional packages.
• Resources: Setup is generally lightweight, but running deep RL models can be computationally intensive, potentially benefiting from GPU acceleration.
• Links: OpenAI Gym

Highlighted Details

• Covers a broad spectrum of RL algorithms from foundational DP to modern Deep RL.
• Includes explanations and code for both value-based and policy-based methods.
• Features practical implementations using OpenAI Gym environments.
• Provides extensive references to key papers and courses for further study.

Maintenance & Community

The repository is maintained by omerbsezer. While specific community channels like Discord/Slack are not explicitly mentioned, the project's comprehensive nature suggests it's a valuable reference for the RL community.

Licensing & Compatibility

The repository's README does not explicitly state a license. Users should verify licensing for any code or resources used, especially for commercial applications.

Limitations & Caveats

This tutorial is stated to be for educational purposes only and not an academic study. The breadth of topics covered means some advanced areas might be introductory.