panda-gym
stable-baselines3
panda-gym | stable-baselines3 | |
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3 | 46 | |
446 | 7,953 | |
- | 3.1% | |
5.3 | 8.2 | |
5 months ago | 5 days ago | |
Python | Python | |
MIT License | MIT License |
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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.
stable-baselines3
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Sim-to-real RL pipeline for open-source wheeled bipeds
The latest release (v3.0.0) of Upkie's software brings a functional sim-to-real reinforcement learning pipeline based on Stable Baselines3, with standard sim-to-real tricks. The pipeline trains on the Gymnasium environments distributed in upkie.envs (setup: pip install upkie) and is implemented in the PPO balancer. Here is a policy running on an Upkie:
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[P] PettingZoo 1.24.0 has been released (including Stable-Baselines3 tutorials)
PettingZoo 1.24.0 is now live! This release includes Python 3.11 support, updated Chess and Hanabi environment versions, and many bugfixes, documentation updates and testing expansions. We are also very excited to announce 3 tutorials using Stable-Baselines3, and a full training script using CleanRL with TensorBoard and WandB.
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[Question] Why there is so few algorithms implemented in SB3?
I am wondering why there is so few algorithms in Stable Baselines 3 (SB3, https://github.com/DLR-RM/stable-baselines3/tree/master)? I was expecting some algorithms like ICM, HIRO, DIAYN, ... Why there is no model-based, skill-chaining, hierarchical-RL, ... algorithms implemented there?
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Stable baselines! Where my people at?
Discord is more focused, and they have a page for people who wants to contribute https://github.com/DLR-RM/stable-baselines3/blob/master/CONTRIBUTING.md
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SB3 - NotImplementedError: Box([-1. -1. -8.], [1. 1. 8.], (3,), <class 'numpy.float32'>) observation space is not supported
Therefore, I debugged this error to the ReplayBuffer that was imported from `SB3`. This is the problem function -
- Exporting an A2C model created with stable-baselines3 to PyTorch
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
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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Stable-Baselines3 v1.8 Release
Changelog: https://github.com/DLR-RM/stable-baselines3/releases/tag/v1.8.0
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
Great project! One question though, is there any reason why you are not using existing RL models instead of creating your own, such as stable baselines?
- Is stable-baselines3 compatible with gymnasium/gymnasium-robotics?
What are some alternatives?
dm_env - A Python interface for reinforcement learning environments
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
dreamerv2 - Mastering Atari with Discrete World Models
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
sapai - Super auto pets engine built with reinforment learning training in mind
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
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]
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
dreamer - Dream to Control: Learning Behaviors by Latent Imagination
tianshou - An elegant PyTorch deep reinforcement learning library.
Safe-panda-gym - OpenaAI Gym Franka Emika Panda robot environment based on PyBullet.
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros