mujoco-py
dm_control
| mujoco-py | dm_control | |
|---|---|---|
| 3 | 7 | |
| 2,739 | 3,549 | |
| 0.4% | 1.6% | |
| 0.0 | 7.5 | |
| 5 months ago | 7 days ago | |
| Cython | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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mujoco-py
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Installation issues with Open AI GYM and Mujoco
I am very confused though I tried to use the documentation here - openai/mujoco-py: MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3. (github.com) but its not working out? Can the experts from this community please advise?
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Installing & Using MuJoCo 2.1.5 with OpenAi Gym
Following the instruction on https://github.com/openai/mujoco-py just with the 2.1.5 version and I get following error when I try to import mujoco_py
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Can anyone help me with MuJoCo installation on Mac with M1 chip ?
I found this issue to be pretty helpful https://github.com/openai/mujoco-py/issues/662.
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?
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).
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
crafter - Benchmarking the Spectrum of Agent Capabilities
lab - A customisable 3D platform for agent-based AI research
myosuite - MyoSuite is a collection of environments/tasks to be solved by musculoskeletal models simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API.
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities