Modularized Implementation of Deep RL Algorithms in PyTorch

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

Implementation of all RL algorithms in a simpler way

High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC

PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..

An index of algorithms for offline reinforcement learning (offline-rl)

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

Inverse RL algorithms (APP, MaxEnt, GAIL, VAIL)

SBX: Stable Baselines Jax (SB3 + Jax) RL algorithms

DRLib：a Concise Deep Reinforcement Learning Library, Integrating HER, PER and D2SR for Almost Off-Policy RL Algorithms.

Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

Structural implementation of RL key algorithms

Implementation of benchmark RL algorithms

RLAnything & DemyAgent: General and scalable agentic RL algorithms across terminal, GUI, SWE, and tool-call settings

DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)

Reinforcement learning theory book about foundations of deep RL algorithms with proofs.

RLStructures is a library to facilitate the implementation of new reinforcement learning algorithms. It includes a library, a tutorial, and different RL algorithms provided as examples.

🤖 Elegant implementations of offline safe RL algorithms in PyTorch

Repository of implementations of classic and sota rl algorithms from scratch in PyTorch

PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.

Platform for evaluating reinforcement learning (RL) algorithms on a physical Atari system.

Clean single-file implementation of offline RL algorithms in JAX

The source code for Performance comparision of Deep RL algorithms for Energy Systems Optimal Scheduling

The RL Reliability Metrics library provides a set of metrics for measuring the reliability of reinforcement learning (RL) algorithms, as well as statistical tools for comparing algorithms and for computing confidence intervals on these metrics.

A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.

Deep Reinforcement Learning (RL) algorithms for underwater target tracking with Autonomous Underwater Vehicles (AUV)

Deep RL Algorithms implemented for UC Berkeley's CS 294-112: Deep Reinforcement Learning

RL algorithms

Re-implementations of SOTA RL algorithms.