Tianshou: a Highly Modularized Deep Reinforcement Learning Library

Alexis Duburcq; Dong Yan; Hang Su; Huayu Chen; Jiayi Weng; Jun Zhu; Kaichao You; Minghao Zhang; Yi Su

Tianshou: a Highly Modularized Deep Reinforcement Learning Library

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2107.14171 v3 pith:ZCI7VLV3 submitted 2021-07-29 cs.LG

Tianshou: a Highly Modularized Deep Reinforcement Learning Library

Jiayi Weng , Huayu Chen , Dong Yan , Kaichao You , Alexis Duburcq , Minghao Zhang , Yi Su , Hang Su

show 1 more author

Jun Zhu

This is my paper

classification cs.LG

keywords tianshoualgorithmsclassicdeephighlylearninglibrarymodularized

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend. Tianshou intends to be research-friendly by providing a flexible and reliable infrastructure of DRL algorithms. It supports online and offline training with more than 20 classic algorithms through a unified interface. To facilitate related research and prove Tianshou's reliability, we have released Tianshou's benchmark of MuJoCo environments, covering eight classic algorithms with state-of-the-art performance. We open-sourced Tianshou at https://github.com/thu-ml/tianshou/.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Gymnasium: A Standard Interface for Reinforcement Learning Environments
cs.LG 2024-07 accept novelty 5.0

Gymnasium establishes a standardized API for RL environments to improve interoperability, reproducibility, and ease of development in reinforcement learning.