This repository provides tutorials for implementing popular reinforcement learning algorithms using PyTorch and OpenAI Gym. It targets developers and researchers seeking practical, code-based explanations of RL concepts, enabling them to build and experiment with agents for various environments.

How It Works

The tutorials implement algorithms like REINFORCE, Actor-Critic, A2C, GAE, and PPO. Each implementation focuses on a clear workflow: environment creation, policy model initialization, state-action-reward loop, and policy updates. The core advantage is the direct, step-by-step implementation of foundational RL algorithms, making complex concepts accessible through PyTorch code.

Quick Start & Requirements

• Install PyTorch: https://pytorch.org/get-started/locally/
• Install Gym: https://github.com/openai/gym
• Python 3.7, PyTorch 1.3, Gym 0.15.4

Highlighted Details

• Tutorials cover foundational algorithms: REINFORCE, Actor-Critic, A2C, GAE, PPO.
• Focus on the CartPole-v1 environment for consistent benchmarking.
• Includes explanations of core RL concepts and workflow.
• References key academic papers and resources for further study.

Maintenance & Community

The project is marked as "[IN PROGRESS]". Feedback is welcomed via GitHub issues.

Licensing & Compatibility

The repository does not explicitly state a license.

Limitations & Caveats

The project is still under development, with potential for changes and additions. Specific versions of PyTorch (1.3) and Gym (0.15.4) are specified, which may require environment management for compatibility.