dopamine
ai-traineree
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| dopamine | ai-traineree | |
|---|---|---|
| 3 | 1 | |
| 10,367 | 24 | |
| 0.4% | - | |
| 4.8 | 0.0 | |
| 23 days ago | about 2 years ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
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.
dopamine
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Fast and hackable frameworks for RL research
I'm tired of having my 200m frames of Atari take 5 days to run with dopamine, so I'm looking for another framework to use. I haven't been able to find one that's fast and hackable, preferably distributed or with vectorized environments. Anybody have suggestions? seed-rl seems promising but is archived (and in TF2). sample-factory seems super fast but to the best of my knowledge doesn't work with replay buffers. I've been trying to get acme working but documentation is sparse and many of the features are broken.
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RL review
You can also reference the source code for some of the popular implementations from open source RL libraries like stablebaselines3, RLlib, CleanRL, or Dopamine. These can help you if you’re trying to compare your implementation to a “standard”.
- Rainbow Library
ai-traineree
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Rainbow Library
Maybe you'll find this easy to use https://github.com/laszukdawid/ai-traineree
What are some alternatives?
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