machin
maze
machin | maze | |
---|---|---|
2 | 4 | |
389 | 258 | |
- | 1.2% | |
1.8 | 0.0 | |
almost 3 years ago | 17 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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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
maze
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[P] Maze: A Framework for Applied Reinforcement Learning
Check out Maze on GitHub - we'd love feedback from anybody with an interest and/or experience in reinforcement learning!
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Maze: A Framework for Applied Reinforcement Learning
Check out Maze on GitHub and its documentation here.
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Is there a consensus about RL frameworks?
For industrial and logistics problems this one looks promising: https://github.com/enlite-ai/maze saw their presentation 2 weeks ago at an international AI conference and was surprised that its already in use and available on github.
- MazeRL - Applied Reinforcement Learning with Python
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
dcss-ai-wrapper - An API for Dungeon Crawl Stone Soup for Artificial Intelligence research.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Apache Impala - Apache Impala
nle - The NetHack Learning Environment
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
dm_env - A Python interface for reinforcement learning environments
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. 🔥