ChainerRL: A Deep Reinforcement Learning Library

Abstract

In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original papers' experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found on GitHub: https://github.com/chainer/chainerrl.

Journal of Machine Learning Research

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• CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
• t-Soft Update of Target Network for Deep Reinforcement Learning
• The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
• d3rlpy: An Offline Deep Reinforcement Learning Library
• PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems
• Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning
• RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation
• Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators
• Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization
• Discriminator Soft Actor Critic without Extrinsic Rewards
• The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning
• Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
• Did I do that? Blame as a means to identify controlled effects in reinforcement learning
• Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations