orion
Asynchronous Distributed Hyperparameter Optimization. (by Epistimio)
dreamerv2
Mastering Atari with Discrete World Models (by danijar)
| orion | dreamerv2 | |
|---|---|---|
| 1 | 4 | |
| 287 | 915 | |
| 0.0% | 2.4% | |
| 7.4 | 0.0 | |
| about 1 year ago | about 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
orion
Posts with mentions or reviews of orion.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Git token, how to I encourage git to have me put in a username and password?
$ git remote show origin [email protected]:Epistimio/orion.git << SSH $ git remote show origin https://github.com/Epistimio/orion.git << HTTPs
dreamerv2
Posts with mentions or reviews of dreamerv2.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-26.
-
Sources of Actor Gradients
In fact, they found that just reinforce gradients work in DM control now too: Dreamerv2 GitHub (they just needed to turn off gradients through the action path - which I guess was being passed back with straight-through estimation? I'm actually having a difficult time telling how the gradient is different on the action vs policy.log_prob(action)).
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PyDreamer: model-based RL written in PyTorch + integrations with DM Lab and MineRL environments
This is my implementation of Hafner et al. DreamerV2 algorithm. I found the PlaNet/Dreamer/DreamerV2 paper series to be some of the coolest RL research in recent years, showing convincingly that MBRL (model-based RL) does work and is competitive with model-free algorithms. And we all know that AGI will be model-based, right? :)
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Any current state or the art libraries for training agents to play atari games?
Last I checked, for running off a single node, the state of the art was Dreamerv2 https://github.com/danijar/dreamerv2
- Google AI, DeepMind And The University of Toronto Introduce DreamerV2, The First Reinforcement Learning (RL) Agent That Outperforms Humans on The Atari Benchmark
What are some alternatives?
When comparing orion and dreamerv2 you can also consider the following projects:
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
dreamerv3 - Mastering Diverse Domains through World Models