Reinforcement Learning Library

Reinforcement learning (RL) libraries are software tools designed to streamline the development and deployment of RL agents, accelerating research and practical applications. Current research emphasizes improving efficiency and scalability through parallelization techniques, creating standardized benchmarks for reproducibility, and developing libraries compatible with diverse hardware (CPUs, GPUs, embedded systems) and frameworks (PyTorch, TensorFlow). These advancements facilitate broader adoption of RL across various domains, from robotics and game playing to complex control systems, by providing researchers and developers with robust, accessible, and high-performance tools.

Papers

November 18, 2024

Syllabus: Portable Curricula for Reinforcement Learning Agents
Ryan Sullivan, Ryan Pégoud, Ameen Ur Rahmen, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P. Dickerson
Reinforcement Learning Curriculum Learning Reinforcement Learning Agent Course Specific Context Reinforcement Learning Library Multi Agent RL Neural MMO

June 11, 2024

PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice
Joseph Suarez
Environment Feature Reinforcement Learning Library Neural MMO

May 19, 2024

Going into Orbit: Massively Parallelizing Episodic Reinforcement Learning
Jan Oberst, Johann Bonneau
Deep Reinforcement Learning Reinforcement Learning Benchmark Orbital Motion Reinforcement Learning Library Parallel Reinforcement Learning

February 27, 2024

Beacon, a lightweight deep reinforcement learning benchmark library for flow control
Jonathan Viquerat, Philippe Meliga, Pablo Jeken, Elie Hachem
Deep Reinforcement Learning Computational Fluid Dynamic Flow Control Reinforcement Learning Library Optimal Beacon

December 25, 2023

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
Wenzhang Liu, Wenzhe Cai, Kun Jiang, Guangran Cheng, Yuanda Wang, Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun
Multi Agent Challenge Reinforcement Learning Library

December 6, 2023

Pearl: A Production-ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu
Reinforcement Learning Reinforcement Learning Library

December 5, 2023

LExCI: A Framework for Reinforcement Learning with Embedded Systems
Kevin Badalian, Lucas Koch, Tobias Brinkmann, Mario Picerno, Marius Wegener, Sung-Yong Lee, Jakob Andert
Reinforcement Learning Artificial Intelligence New Framework Reinforcement Learning Agent Embedded System Reinforcement Learning Library

October 20, 2023

RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup
Nico Bohlinger, Klaus Dorer
Deep Reinforcement Learning Reinforcement Learning Benchmark Robocup SPL Reinforcement Learning Library

July 5, 2023

Stability of Q-Learning Through Design and Optimism
Sean Meyn
Product Design Q Learning Core Stability Stochastic Approximation Linear Function Approximation Reinforcement Learning Library

June 6, 2023

RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Algorithm Continuous Control Tiny Machine Learning Reinforcement Learning Library

March 3, 2023

CoRL: Environment Creation and Management Focused on System Integration
Justin D. Merrick, Benjamin K. Heiner, Cameron Long, Brian Stieber, Steve Fierro, Vardaan Gangal, Madison Blake, Joshua Blackburn
Simulation Environment New Environment Reinforcement Learning Library

December 1, 2022

Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox
Qiyue Yin, Tongtong Yu, Shengqi Shen, Jun Yang, Meijing Zhao, Kaiqi Huang, Bin Liang, Liang Wang
Timely Survey Deep Reinforcement Learning Multi Agent Multi Agent Learning Reinforcement Learning Library

October 3, 2022

CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement Learning Easy to Use Toolkit Reinforcement Learning Research Reinforcement Learning Library Sustainable Artificial Intelligence

February 28, 2022

Avalanche RL: a Continual Reinforcement Learning Library
Nicolò Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu
Continual Learning Continual Reinforcement Learning Reinforcement Learning Library Habitat Simulator

January 24, 2022

Pearl: Parallel Evolutionary and Reinforcement Learning Library
Rohan Tangri, Danilo P. Mandic, Anthony G. Constantinides
Reinforcement Learning Evolutionary Computation Reinforcement Learning Library Open Source Reinforcement Learning