dm_control
lab
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| dm_control | lab | |
|---|---|---|
| 7 | 4 | |
| 3,528 | 7,002 | |
| 2.2% | 0.7% | |
| 7.5 | 0.0 | |
| 4 days ago | over 1 year ago | |
| Python | C | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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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.
dm_control
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
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.
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Any beginner resources for RL in Robotics?
DeepMind's dm control: https://github.com/deepmind/dm_control
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
What are some alternatives?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments
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).
mujoco-py - MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
Robotics Library (RL) - The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.
acme - A library of reinforcement learning components and agents
dreamerv2 - Mastering Atari with Discrete World Models
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
crafter - Benchmarking the Spectrum of Agent Capabilities
CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library