pydreamer
dreamerv2
| pydreamer | dreamerv2 | |
|---|---|---|
| 3 | 4 | |
| 194 | 853 | |
| - | - | |
| 0.0 | 0.0 | |
| about 1 year ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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pydreamer
dreamerv2
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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?
dreamer - Dream to Control: Learning Behaviors by Latent Imagination
dreamerv3 - Mastering Diverse Domains through World Models
panda-gym - Set of robotic environments based on PyBullet physics engine and gymnasium.
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
planet - Learning Latent Dynamics for Planning from Pixels
orion - Asynchronous Distributed Hyperparameter Optimization.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
crafter - Benchmarking the Spectrum of Agent Capabilities
opendr - A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).