IsaacGymEnvs
gym3
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| IsaacGymEnvs | gym3 | |
|---|---|---|
| 8 | 3 | |
| 1,599 | 139 | |
| 8.2% | 0.7% | |
| 4.6 | 0.0 | |
| 3 days ago | about 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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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.
gym3
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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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[N] Gym 0.19.0 (the first big maintenance release) is now out
[1] https://github.com/openai/gym3/
- OpenAI gym3 Design Choices
What are some alternatives?
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.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
Unity-Robotics-Hub - Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.
OmniIsaacGymEnvs - Reinforcement Learning Environments for Omniverse Isaac Gym
skrl - Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym
orbit - Unified framework for robot learning built on NVIDIA Isaac Sim
awesome-isaac-gym - A curated list of awesome NVIDIA Issac Gym frameworks, papers, software, and resources