This repository provides a comprehensive set of tutorials and code examples for various Reinforcement Learning (RL) algorithms. It targets individuals seeking to learn and implement RL concepts, offering practical implementations from foundational algorithms to more advanced techniques.

How It Works

The project offers implementations of key RL algorithms including Deep Q-Learning (DQN), Double DQN (DDQN), Dueling DDQN (D3QN), Policy Gradients (PG/REINFORCE), Actor-Critic (A2C, A3C), and Proximal Policy Optimization (PPO). These are demonstrated with popular OpenAI Gym environments like Pong, LunarLander, and BipedalWalker, showcasing both discrete and continuous action spaces.

Quick Start & Requirements

• Install via pip install -r requirements.txt.
• Requires Python 3.x and TensorFlow 2.3.1.
• OpenAI Gym is a core dependency.

Highlighted Details

• Tutorials cover DQN, DDQN, D3QN, D3QN with Prioritized Experience Replay (PER), and D3QN with Convolutional Neural Networks.
• Includes implementations for Policy Gradient, Actor-Critic (A2C, A3C), and Proximal Policy Optimization (PPO).
• Demonstrates PPO for discrete (Pong) and continuous (LunarLander, BipedalWalker) action spaces.
• Features a "learn from scratch" PPO rocket landing agent tutorial.

Maintenance & Community

No specific information on maintainers, community channels, or roadmap is available in the provided README.

Licensing & Compatibility

The repository does not explicitly state a license.

Limitations & Caveats

The project specifies TensorFlow 2.3.1, which is an older version and may have compatibility issues with newer TensorFlow releases or other libraries. The lack of explicit licensing information could pose a barrier to commercial use or integration into closed-source projects.