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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An elegant PyTorch deep reinforcement learning library.
| Date | Stars |
|---|---|
| 2026-07-24 | 10882 |
| 2026-07-25 | 10883 |
| 2026-07-28 | 10883 |
| 2026-07-30 | 10883 |
| 2026-07-31 | 10899 |
| 2026-08-01 | 10904 |
| 2026-08-02 | 10905 |
| 2026-08-03 | 10907 |
| 2026-08-04 | 10908 |
| 2026-08-05 | 10909 |
| 2026-08-06 | 10909 |
Today
— stars today
This week
+26 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.24%/day
<div align="center"> <a href="http://tianshou.readthedocs.io"><img width="300px" height="auto" src="https://github.com/thu-ml/tianshou/raw/master/docs/_static/images/tianshou-logo.png"></a> </div> --- [](https://pypi.org/project/tianshou/) [](https://tianshou.org/en/master/) [](https://github.com/thu-ml/tianshou/actions) [](https://codecov.io/gh/thu-ml/tianshou) [](https://github.com/thu-ml/tianshou/issues) [](https://github.com/thu-ml/tianshou/stargazers) [](https://github.com/thu-ml/tianshou/network) [](https://github.com/thu-ml/tianshou/blob/master/LICENSE) > [!NOTE] > **Tianshou version 2 is here!** > > We have released the new major version of Tianshou on PyPI. > Version 2 is a complete overhaul of the software design of the procedural API, in which > * we establish a clear separation between learning algorithms and policies (via the separate abstractions `Algorithm` and `Policy`). > * we provide more well-defined, more usable interfaces with extensive documentation of all algorithm and trainer parameters, > renaming some parameters to make their names more consistent and intuitive. > * the class hierarchy is fully revised, establishing a clear separation between on-policy, off-policy and offline algorithms > at the type level and ensuring that all inheritance relationships are meaningful. > > Because of the extent of the changes, this version is not backwards compatible with previous versions of Tianshou. > For migration information, please see the [change log](CHANGELOG.md). **Tianshou** ([天授](https://baike.baidu.com/item/%E5%A4%A9%E6%8E%88)) is a reinforcement learning (RL) library based on pure PyTorch and [Gymnasium](http://github.com/Farama-Foundation/Gymnasium). Tianshou's main features at a glance are: 1. Modular low-level interfaces for algorithm developers (RL researchers) that are both flexible, hackable and type-safe. 1. Convenient high-level interfaces for applications of RL (training an implemented algorithm on a custom environment). 1. Large scope: online (on- and off-policy) and offline RL, experimental support for multi-agent RL (MARL), experimental support for model-based RL, and more Unlike other reinforcement learning libraries, which may have complex codebases, unfriendly high-level APIs, or are not optimized for speed, Tianshou provides a high-performance, modularized framework and user-friendly interfaces for building deep reinforcement learning agents. One more aspect that sets Tianshou apart is its generality: it supports online and offline RL, multi-agent RL, and model-based algorithms. Tianshou aims at enabling concise implementations, both for researchers and practitioners, without sacrificing flexibility. Supported algorithms include: - [Deep Q-Network (DQN)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) - [Double DQN](https://arxiv.org/pdf/1509.06461.pdf) - [Dueling DQN](https://arxiv.org/pdf/1511.06581.pdf) - [Branching DQN](https://arxiv.org/pdf/1711.08946.pdf) - [Categorical DQN (C51)](https://arxiv.org/pdf/1707.06887.pdf) - [Rainbow DQN (Rainbow)](https://arxiv.org/pdf/1710.02298.pdf) - [Quantile Regression DQN (QRDQN)](https://arxiv.org/pdf/1710.10044.pdf) - [Implicit Quantile Network (IQN)](https://arxiv.org/pdf/1806.06923.pdf) - [Fully-parameterized Quantile Function (FQF)](https://arxiv.org/pdf/1911.02140.pdf) - [Policy Gradient (PG)](https://papers.nips.cc/paper/1713-policy-gradient-met
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Dr. Dominik Jain · Oraios AI (@oraios) · Germany
524
Michael Panchenko · Oraios AI · Germany
282
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51
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youkaichao · @vllm-project · China
22
Alexis DUBURCQ
21
Yi Su · United States
20
10
Markus Krimmel
7
Daniel Plop
7
6
6
5
5
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rocknamx · China
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Rocco
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:006d91392bc7d027, topic:rl, topic:imitation-learning, desc:reinforcement learning