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Tianshou: a Highly Modularized Deep Reinforcement Learning Library

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

classification cs.LG
keywords tianshoualgorithmsclassicdeephighlylearninglibrarymodularized
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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/.

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Cited by 1 Pith paper

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  1. When Maximum Entropy Misleads Policy Optimization

    cs.LG 2025-06 reject novelty 6.0 of 10

    Maximum entropy RL can be formally steered into arbitrary suboptimal policies at convergence by adding entropy trap states, while standard RL is unaffected.

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