ChainerRL: A Deep Reinforcement Learning Library

TL;DR

ChainerRL is an open-source Python library built on Chainer that implements a wide range of deep reinforcement learning algorithms, facilitates reproducible research, and provides visualization tools for analyzing trained agents.

Contribution

It introduces a comprehensive, reproducible, and user-friendly DRL library with visualization capabilities, based on state-of-the-art algorithms.

Findings

Reproduces benchmark results for multiple algorithms

Provides tools for qualitative inspection of agents

Supports a wide range of DRL techniques

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.