panda-gym
dm_env
| panda-gym | dm_env | |
|---|---|---|
| 3 | 2 | |
| 446 | 329 | |
| - | 0.0% | |
| 5.3 | 0.0 | |
| 5 months ago | over 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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panda-gym
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Hyperparameters for pick&place with Franka Emika manipulator
I'm trying to solve pick&place (and possibly also the other tasks in this repository) with Franka Emika Panda manipulator implemented in Mujoco. I've tried for long with stable_baseline3 but without any results, someone told me to try with RLLib because has better implementation (?), but still I can't find any solution...
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SAFE-PANDA-GYM a modification to Panda - Gym to train Safe-RL agents
We develop a modification to the Panda Gym by adding constraints to the environments like Unsafe regions and, constraints on the task. The aim is to develop an environment to test CMDPs (Constraint Markov Decision Process) / Safe-RL algorithms such as CPO, PPO - Lagrangian and algorithms developed by the team. Agents would not only have to come up with optimal policy for control and planning but also ensure they don't violate a constraint.
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Did anyone try Panda-Gym?
The acquisition of Mujoco makes Openai to remove the robotics from their repo. I had no choice but to find an alternative. Then I found https://github.com/qgallouedec/panda-gym which is built on PyBullet.
dm_env
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Worthwhile to convert custom env to be dm_env compatible?
Can anyone speak to their experience using acme (https://github.com/deepmind/acme) and by extension dm_env (https://github.com/deepmind/dm_env)? I'm wondering if it would be worthwhile for me to invest the time into converting my custom environment (which loosely follows the standard RL setup) over to this format.
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[D] What would a "Production" RL stack look like in terms of tooling?
An interface based loosely on the standard RL setup. I'm thinking about adapting it to fit dm_env (https://github.com/deepmind/dm_env) to let it do more heavy lifting since I quite like Haiku, rlax and the rest of what they do.
What are some alternatives?
dreamerv2 - Mastering Atari with Discrete World Models
acme - A library of reinforcement learning components and agents
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
sapai - Super auto pets engine built with reinforment learning training in mind
maze - Maze Applied Reinforcement Learning Framework
habitat-api - A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators. [Moved to: https://github.com/facebookresearch/habitat-lab]
machine_learning_examples - A collection of machine learning examples and tutorials.
dreamer - Dream to Control: Learning Behaviors by Latent Imagination
Safe-panda-gym - OpenaAI Gym Franka Emika Panda robot environment based on PyBullet.
Gymnasium-Robotics - A collection of robotics simulation environments for reinforcement learning