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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.
Massively Parallel Deep Reinforcement Learning. π₯
| Date | Stars |
|---|---|
| 2026-07-24 | 4352 |
| 2026-07-25 | 4352 |
| 2026-07-28 | 4352 |
| 2026-07-30 | 4352 |
| 2026-07-31 | 4352 |
| 2026-08-04 | 4352 |
| 2026-08-05 | 4351 |
| 2026-08-06 | 4352 |
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<div align="center"> <img align="center" width="30%" alt="image" src="https://github.com/AI4Finance-Foundation/FinGPT/assets/31713746/e0371951-1ce1-488e-aa25-0992dafcc139"> </div> ## ElegantRL βε°ι β: Massively Parallel Deep Reinforcement Learning [](https://pepy.tech/project/elegantrl) [](https://pepy.tech/project/elegantrl) [](https://www.python.org/downloads/release/python-360/) [](https://pypi.org/project/elegantrl/)      <br/> <a href="https://github.com/AI4Finance-Foundation/ElegantRL" target="\_blank"> <div align="center"> <img src="figs/icon.jpg" width="40%"/> </div> <!-- <div align="center"><caption>Slack Invitation Link</caption></div> --> </a> <br/> ElegantRL is a lightweight and structurally clean reinforcement learning framework designed to express core RL algorithms with minimal complexity and maximal clarity. The name βElegantβ reflects its philosophy: small in dependency footprint, yet elegant in code structure. The framework avoids unnecessary third-party libraries while maintaining modular design, mathematical transparency, and engineering readability. ElegantRL focuses on implementing reinforcement learning algorithms in their pure form β clear, extensible, and efficient β without sacrificing performance or simplicity.  [](https://discord.gg/trsr8SXpW5) ElegantRL ([website](https://elegantrl.readthedocs.io/en/latest/index.html)) is developed for users/developers with the following advantages: - **Cloud-native**: follows a cloud-native paradigm through micro-service architecture and containerization, and supports [ElegantRL-Podracer](https://elegantrl.readthedocs.io/en/latest/tutorial/elegantrl-podracer.html) and [FinRL-Podracer](https://elegantrl.readthedocs.io/en/latest/tutorial/finrl-podracer.html). - **Scalable**: fully exploits the parallelism of DRL algorithms, making it easily scale out to hundreds or thousands of computing nodes on a cloud platform, say, a [DGX SuperPOD platform](https://www.nvidia.com/en-us/data-center/dgx-superpod/) with thousands of GPUs. - **Elastic**: allows to elastically and automatically allocate computing resources on the cloud. - **Lightweight**: the core codes have <1,000 lines (check [Elegantrl_Helloworld](https://github.com/AI4Finance-Foundation/ElegantRL/tree/master/helloworld)). - **Efficient**: in many testing cases (e.g., single-GPU/multi-GPU/GPU-cloud), we find it more efficient than [Ray RLlib](https://github.com/ray-project/ray). - **Stable**: much much much more stable than [Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3) by utilizing various methods such as the Hamiltonian term. - **Practical**: used in multipe projects ([FinRL](https://github.com/AI4Finance-Foundation/FinRL), [FinRL-Meta](https://github.com/AI4Finance-Foundation/FinRL-Meta), etc.) - **Massively parallel simulations** are used in multipe projects ([FinRL](https://github.com/AI4Finance-Foundation/FinRL), etc.); therefore, the sampling speed is high since we can build many many GPU-based environments. ElegantRL implements the fol
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YonV1943 ζΎδΌθ¨ Β· SIAT(δΈη§ι’ζ·±ε³ε θΏι’)
749
Ming Zhu
629
Steven Li
532
Yanglet Β· United States
276
229
Eric
106
Joey Xia Β· Columbia University
83
Momin Haider
80
Markus K.
68
ByFinTech Β· AI4Finance Foundation Inc. Β· United States
56
Xinghang Sun
13
11
Orion.Zou Β· in home Β· China
9
Vincent
8
7
Jingyang Rui (Ray) Β· The University of Hong Kong Β· China
6
6
Bobby
5
Zeliang Zhang
4
2
Would you bet a product on this? Bounded 0β100 and slow moving.
matched fp:3eec9f3420acad01, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:3eec9f3420acad01, topic:pytorch