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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.
A Comprehensive Reinforcement Learning Zoo for Simple Usage 🚀
| Date | Stars |
|---|---|
| 2026-07-24 | 640 |
| 2026-07-25 | 640 |
| 2026-07-28 | 640 |
| 2026-07-30 | 640 |
| 2026-07-31 | 640 |
| 2026-08-06 | 640 |
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# Reinforcement Learning Zoo [](https://rlzoo.readthedocs.io/en/latest/?badge=latest) [](https://github.com/tensorflow/tensorflow/releases) [](http://pepy.tech/project/rlzoo) <br/> <a href="https://deepreinforcementlearningbook.org" target="\_blank"> <div align="center"> <img src="docs/img/rlzoo-logo.png" width="40%"/> </div> <!-- <div align="center"><caption>Slack Invitation Link</caption></div> --> </a> <br/> RLzoo is a collection of the most practical reinforcement learning algorithms, frameworks and applications. It is implemented with Tensorflow 2.0 and API of neural network layers in [**TensorLayer2.0+**](https://github.com/tensorlayer/tensorlayer), to provide a hands-on fast-developing approach for reinforcement learning practices and benchmarks. It supports basic toy-tests like [OpenAI Gym](https://gym.openai.com/) and [DeepMind Control Suite](https://github.com/deepmind/dm_control) with very simple configurations. Moreover, RLzoo supports robot learning benchmark environment [RLBench](https://github.com/stepjam/RLBench) based on [Vrep](http://www.coppeliarobotics.com/)/[Pyrep](https://github.com/stepjam/PyRep) simulator. Other large-scale distributed training framework for more realistic scenarios with [Unity 3D](https://github.com/Unity-Technologies/ml-agents), [Mujoco](http://www.mujoco.org/), [Bullet Physics](https://github.com/bulletphysics/bullet3), etc, will be supported in the future. A [Springer textbook](https://deepreinforcementlearningbook.org) is also provided, you can get the free PDF if your institute has Springer license. Different from RLzoo for simple usage with **high-level APIs**, we also have a [RL tutorial](https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning) that aims to make the reinforcement learning tutorial simple, transparent and straight-forward with **low-level APIs**, as this would not only benefits new learners of reinforcement learning, but also provide convenience for senior researchers to testify their new ideas quickly. <!-- <em>Gym: Atari</em> <em>Gym: Box2D </em> <em>Gym: Classic Control </em> <em>Gym: MuJoCo </em>--> <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/atari.gif" height=250 width=205 > <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/box2d.gif" height=250 width=205 ><img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/classic.gif" height=250 width=205 > <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/mujoco.gif" height=250 width=205 > <!-- <em>Gym: Robotics</em> <em>DeepMind Control Suite </em> <em>Gym: RLBench </em> --> <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/robotics.gif" height=250 width=205 > <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/dmcontrol.gif" height=250 width=205 > <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/rlbench.gif" height=250 width=205 > <img src="https://github.com/tensorlayer/tensorlayer/blob/master/img/tl_transparent_logo.png" height=180 width=200 > Please check our [**Online Documentation**](https://rlzoo.readthedocs.io) for detailed usage and the [**Arxiv paper**](https://arxiv.org/abs/2009.08644) for the high-level description of design choices plus comparisons with other RL libraries. We suggest users to report bugs using Github issues. Users can also discuss how to use RLzoo in the following slack channel. <br/> <a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtODk1NTQ5NTY1OTM5LTQyMGZhN2UzZDBhM2I3YjYzZDBkNGExYzcyZDNmOGQzNmYzNjc3ZjE3MzhiMjlkMmNiMmM3Nzc4ZDY2YmNkMTY" target="\_blank"> <div align="center"> <img src="https://github.com/tensorlayer/tensorlayer/raw/master/img/join_slack.png" width="40%"/> </div> </a> <br/> [*News*] RLzoo's p
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Zihan Ding
211
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Hao · Peking University · China
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Yanhua Huang · Xiaohongshu · China
15
Jiarong Han · Peng Cheng Lab · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2ca479a62780c6ff, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning
matched fp:2ca479a62780c6ff, topic:deep-learning, topic:tensorflow, readme:training framework