IsaacGymEnvs
skrl
| IsaacGymEnvs | skrl | |
|---|---|---|
| 8 | 7 | |
| 1,616 | 404 | |
| 4.4% | - | |
| 3.9 | 9.3 | |
| 13 days ago | 15 days 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.
skrl
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Isaac Gym with Off-policy Algorithms
skrl will allow you to easily configure and use off-policy algorithms such as DDPG, TD3 and SAC in Isaac Gym, Omniverse Isaac Gym and Isaac Orbit, but I think there will not be significant gains compared to on-policy algorithms.
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Choosing a framework in 2023
Check its comprehensive documentation at https://skrl.readthedocs.io
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Best recurrent RL library?
Also, skrl. It supports RNN, LSTM, GRU, and other variants for A2C, DDPG, PPO, SAC, TD3, and TRPO agents. See the models basic usage and examples
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What is the limit on parallel environments?
In this case, I encourage you to try the skrl RL library that fully supports all of them, among others.
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What's the best "Non-Black Box" framework for SOTA algorithms?
I encourage you to try skrl (https://skrl.readthedocs.io).
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I have a PPO implementation but I am pretty sure it wrong. I need this correct because I would like to add LSTM layer over this. Could someone have a look?
I encourage you to take a look at the skrl library...
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Can we use RNN in RL?
This is the list of examples (to be included in the documentation) that includes RNN: (ddpg_gym_pendulumnovel_gru.py, ddpg_gym_pendulumnovel_lstm.py, ddpg_gym_pendulumnovel_rnn.py, etc.)... and here are some RNN benchmarking results (to be updated for the release)
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.
awesome-isaac-gym - A curated list of awesome NVIDIA Issac Gym frameworks, papers, software, and resources
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
pfrl - PFRL: a PyTorch-based deep reinforcement learning library
robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
OmniIsaacGymEnvs - Reinforcement Learning Environments for Omniverse Isaac Gym
Unity-Robotics-Hub - Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.
pomdp-baselines - Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
gym3 - Vectorized interface for reinforcement learning environments
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
autonomous-learning-library - A PyTorch library for building deep reinforcement learning agents.