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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. 🔥
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Today
+2 stars today
This week
+5 stars this week
This month
+9 stars this month
Momentum
0.0
growth rate 0.11%/day
<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 “小雅”: 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 “Elegant” 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 — clear, extensible, and efficient — 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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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3eec9f3420acad01, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:3eec9f3420acad01, topic:pytorch