This repository provides a clean, robust, and unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms. It targets researchers and practitioners in DRL who need a standardized codebase for experimenting with and comparing various state-of-the-art algorithms. The benefit is a single, well-organized repository covering a wide range of DRL techniques, reducing the effort required to set up and run different algorithms.

How It Works

The project implements algorithms such as Q-learning, DQN variants (Double DQN, Prioritized Experience Replay, C51, Noisy DQN), policy gradient methods (PPO), actor-critic methods (DDPG, TD3, SAC), and Actor-Sharer-Learner (ASL). Each algorithm is housed in its own directory with a main.py script for execution, promoting modularity and ease of use. The implementation leverages PyTorch for neural network definitions and training loops, with gymnasium as the primary simulation environment interface.

Quick Start & Requirements

• Primary install/run command: Navigate to an algorithm's folder and run python main.py.
• Prerequisites: gymnasium==0.29.1, numpy==1.26.1, pytorch==2.1.0, python==3.11.5.
• Setup time: Minimal, primarily involves installing Python dependencies.
• More details: README.md files within each algorithm's folder.

Highlighted Details

• Comprehensive coverage of 11 popular DRL algorithms.
• Includes implementations for both discrete and continuous action spaces.
• Supports various environments like CartPole, LunarLander, Atari games, and Pendulum.
• Provides links to relevant papers, books, courses, and blogs for further learning.

Maintenance & Community

The repository appears to be a personal project with no explicit mention of contributors, sponsorships, or community channels like Discord/Slack.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README.

Limitations & Caveats

The README does not specify the license, which could impact commercial use or integration into closed-source projects. There is no explicit mention of testing on different hardware configurations or operating systems beyond the Python dependencies.