lab
dm_control
| lab | dm_control | |
|---|---|---|
| 4 | 7 | |
| 7,002 | 3,549 | |
| 0.0% | 1.6% | |
| 0.0 | 7.5 | |
| over 1 year ago | 3 days ago | |
| C | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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lab
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
- Environments that require long-term memory and reasoning
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Keys doors puzzle in dmlab30
dmlab30 is a test suite of 30 environments for Deep RL research, maintained by DeepMind. https://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30#readme
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[R] Would DeepMind Control Suite be enough for a publication?
DeepMind Lab is also interesting: https://deepmind.com/blog/article/open-sourcing-deepmind-lab https://github.com/deepmind/lab
dm_control
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
Have you ever wanted to use dm-control with stable-baselines3? Within Reinforcement learning (RL), a number of APIs are used to implement environments, with limited ability to convert between them. This makes training agents across different APIs highly difficult, and has resulted in a fractured ecosystem.
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Installing & Using MuJoCo 2.1.5 with OpenAi Gym
Deepmind Control Suite is a good alternative to Open AI Gym for continuous control tasks. It contains many of the environments present in Gym and also a few extra ones. Deepmind Control Suite also uses Mujoco. I found the installation to be straightforward. Check out https://github.com/deepmind/dm_control
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Is there a way to get PPO controlled agents to move a little more gracefully?
Do you know if this is implemented in code anywhere? I've been digging around in DeepMind's dm_control for the past few hours and I haven't found it. I'm not sure what I'm looking for either.
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[D] MuJoCo vs PyBullet? (esp. for custom environment)
If you're interested in using Mujoco, I'd suggest checking out the dm_control package for Python bindings rather than interfacing with C++ directly. I think one downside to Mujoco currently is that you cannot dynamically add objects, and the entire simulation is initialized and loaded according to the MJCF / XML file.
- How to use MuJoCo from Python3
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Any beginner resources for RL in Robotics?
DeepMind's dm control: https://github.com/deepmind/dm_control
What are some alternatives?
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
gym - A toolkit for developing and comparing reinforcement learning algorithms.
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baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments
CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
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).
dm_memorytasks - A set of 13 diverse machine-learning tasks that require memory to solve.
mujoco-py - MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Robotics Library (RL) - The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.