XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

An elegant PyTorch deep reinforcement learning library.

Massively Parallel Deep Reinforcement Learning. 🔥

A collection of my implemented RL agents for games like Pacman, Pong, SpaceInvaders, Frozenlake, Taxi, Pixelcopter, Pyramids and a lot more by implementing various DRL algorithms using gym, unity-ml, pygame, sb3, rl-zoo and pandagym libraries. To see more advanced & interesting agents, please visit below link:

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

This project provides a comprehensive understanding of reinforcement learning, focusing on Actor Critic Algorithms. It involves exploring the OpenAI Gym library, implementing the A2C algorithm from DeepMind's seminal paper, and enhancing the A2C algorithm for improved performance and stability.

RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code

Snake game environment integrated with OpenAI Gym. Proximal Policy Optimization (PPO) implementation for training. Visualization of training progress and agent performance. Easy to understand code.

The simplest implementation of Pensieve (SIGCOMM' 17) via state-of-the-art RL algorithms, including PPO, DQN, SAC, and support for both TensorFlow and PyTorch.

Reinforcement learning algorithms

RL algorithm for stock trading with multiple reward functions

Modularized Implementation of Deep RL Algorithms in PyTorch

SAC, PPO, A2C implementation on Mujoco environments : Humanoid-v4, Ant-v4, Cheetah-v4 . Includes reward manipulation.

A PyTorch library for building deep reinforcement learning agents.

Deep reinforcement learning experiments

A library for ready-made reinforcement learning agents and reusable components for neat prototyping

Reinforcement Learning for Elden Ring on Windows11

Space Invaders agent trained using DQN/A2C models on OpenAI Gym Atari Environment.

RLToolkit is a flexible and high-efficient reinforcement learning framework. Include implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Third homework for the Reinforcement Learning course