bagua
machin
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| bagua | machin | |
|---|---|---|
| 6 | 2 | |
| 865 | 381 | |
| 0.0% | - | |
| 4.8 | 1.8 | |
| 9 months ago | over 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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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.
bagua
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?
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
optuna - A hyperparameter optimization framework
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
PERSIA - High performance distributed framework for training deep learning recommendation models based on PyTorch.
Apache Impala - Apache Impala
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
modin - Modin: Scale your Pandas workflows by changing a single line of code
tianshou - An elegant PyTorch deep reinforcement learning library.
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.
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥