crafter
dreamerv2
Our great sponsors
crafter | dreamerv2 | |
---|---|---|
2 | 4 | |
348 | 853 | |
- | - | |
5.4 | 0.0 | |
3 months ago | over 1 year ago | |
Python | Python | |
MIT License | MIT License |
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.
crafter
-
[R] Benchmarking the Spectrum of Agent Capabilities: They introduce Crafter, an open world survival game with visual inputs that evaluates a wide range of general abilities within a single environment.
Repo for Crafter: https://github.com/danijar/crafter
- Crafter - Open world survival environment for reinforcement learning.
dreamerv2
-
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)).
-
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? :)
-
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?
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
dreamerv3 - Mastering Diverse Domains through World Models
ma-gym - A collection of multi agent environments based on OpenAI gym.
dreamer - Dream to Control: Learning Behaviors by Latent Imagination
FoliClientInstaller - Installs the Foli Client Minecraft modpack
panda-gym - Set of robotic environments based on PyBullet physics engine and gymnasium.
ITAQA-air-quality-aggregator - Framework to collect, aggregate and process air pollution data from different sources (Italy regions)
portfolio-management - Repository for portfolio management using Pytorch, SQLAlchemy and XArray. The management is done using the reinforcement learning algorithm "Soft Actor-Critic".
stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
ecobalyse - Accélerer la mise en place de l'affichage environnemental
planet - Learning Latent Dynamics for Planning from Pixels