dm_control
IsaacGymEnvs
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| dm_control | IsaacGymEnvs | |
|---|---|---|
| 7 | 8 | |
| 3,540 | 1,616 | |
| 2.5% | 9.3% | |
| 7.5 | 3.9 | |
| 3 days ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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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
IsaacGymEnvs
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What is the limit on parallel environments?
Although Gym/Gymnasium allows you to generate vectorized parallel environments, if you want to train in hundreds or thousands of environments you will need to use the NVIDIA simulator repertoire (Isaac Gym, Isaac Orbit or Omniverse Isaac Gym).
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How to optimize custom gym environment for GPU
Otherwise, I'd suggest checking out the Isaac Gym paper and the Isaac Gym Envs repo.
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Showing the "good" values does not help the PPO algorithm?
in the given environment (https://github.com/NVIDIA-Omniverse/IsaacGymEnvs/blob/main/isaacgymenvs/tasks/franka_cabinet.py), the task for the robot is to open a cabinet. The action values, which are the output of the agent, are the target velocity values for the robot's joints.
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Has anyone experience using/implementing "masking action" in Isaac Gym?
can it be implemented in the task-level scripts (i.e. ant.py, FrankaCabinet.py etc.) like this?
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[Material advice] Learn reinforcement leanring
IsaacGymEnvs
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Simulating robotic arm for object manipulation
And here are some reinforcment learning examples.
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What Happened to OpenAI + RL?
Gym has been great at standardizing API and providing a baseline set of environments. However, parallelizing environments with original Gym interface is cumbersome, and new simulators are being introduced with their own ways of doing things. It's not clear to me that Gym is still useful today, from a research perspective.
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[D] MuJoCo vs PyBullet? (esp. for custom environment)
If you already have experience in PyBullet then its probably not worth switching to Mujoco for creating custom environments. However, if you have the GPU compute for it, I'd recommend checking out Isaac Gym. GPU acceleration is great for spawning a bunch of envs for domain randomization, and it's already been used by recent research to get some great results that have previously taken a ridiculous amount of CPU compute.
What are some alternatives?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
MuJoCo_RL_UR5 - A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.
baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
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).
Unity-Robotics-Hub - Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.
mujoco-py - MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
gym3 - Vectorized interface for reinforcement learning environments
Robotics Library (RL) - The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.
OmniIsaacGymEnvs - Reinforcement Learning Environments for Omniverse Isaac Gym
acme - A library of reinforcement learning components and agents
skrl - Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym