ElegantRL
machin
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| ElegantRL | machin | |
|---|---|---|
| 6 | 2 | |
| 3,436 | 381 | |
| 3.2% | - | |
| 7.4 | 1.8 | |
| 6 days ago | over 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ElegantRL
- Does “massively parallel simulation” help advance Reinforcement Learning?
- ElegantRL: Cloud-Native Deep Reinforcement Learning
- ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library
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[R] ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library
The ElegantRL library is featured with “elegant” in the following aspects:
- Lightweight, Efficient and Stable DRL Library
- Lightweight, Efficient and Stable DRL Implementation Using PyTorch
machin
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Best PyTorch RL library for doing research
Machin is really nice, it is very easy to use and to try different things, although it’s developed by one person and maybe not appropriately tested yet.
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Is there a consensus about RL frameworks?
I found this repo very helpful to get started: https://github.com/iffiX/machin
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
tianshou - An elegant PyTorch deep reinforcement learning library.
Apache Impala - Apache Impala
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
Deep-Reinforcement-Learning-Algorithms - 32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
pytorch-ddpg - Deep deterministic policy gradient (DDPG) in PyTorch 🚀
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.