RL-Adventure
maze
| RL-Adventure | maze | |
|---|---|---|
| 3 | 4 | |
| 2,950 | 258 | |
| - | 1.2% | |
| 0.0 | 0.0 | |
| over 2 years ago | 18 days ago | |
| Jupyter Notebook | Python | |
| - | GNU General Public License v3.0 or later |
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RL-Adventure
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Applied resources in Pytorch?
can check this out.
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Is there a consensus about RL frameworks?
For me, nothing beats RL-Adventure. It's not perfect, it has bugs, but it's super readable and will let you level up through the different main methods.
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[N] 20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples
Lecture 7: Function approximation. (slides, code, video)
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.
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
nle - The NetHack Learning Environment
RL-Adventure-2 - Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters [Moved to: https://github.com/higgsfield-ai/higgsfield]
dm_env - A Python interface for reinforcement learning environments
flux-beamer - Flux is a modern style beamer presentation.
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.