ChainerRL: A Deep Reinforcement Learning Library

被引:0
作者
Fujita, Yasuhiro [1 ]
Nagarajan, Prabhat [1 ]
Kataoka, Toshiki [1 ]
Ishikawa, Takahiro [2 ]
机构
[1] Preferred Networks, Tokyo, Japan
[2] Univ Tokyo, Tokyo, Japan
关键词
reinforcement learning; deep reinforcement learning; reproducibility; open source software; chainer; ENVIRONMENT;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1 / 14
页数:14
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