dm_control
Shimmy
| dm_control | Shimmy | |
|---|---|---|
| 7 | 1 | |
| 3,549 | 115 | |
| 1.6% | 0.9% | |
| 7.5 | 6.2 | |
| 6 days ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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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
Shimmy
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
Check out our blog post or release notes for more details. Our documentation website at https://shimmy.farama.org/, containing an overview of each environment with screenshots, installation instructions, full usage scripts, and API information.
What are some alternatives?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
lab - A customisable 3D platform for agent-based AI research
baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
bsuite - bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments
meltingpot - A suite of test scenarios for multi-agent reinforcement 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).
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.
Arcade-Learning-Environment - The Arcade Learning Environment (ALE) -- a platform for AI research.