CausalWorld
stable-baselines3
| CausalWorld | stable-baselines3 | |
|---|---|---|
| 1 | 46 | |
| 201 | 7,953 | |
| 2.5% | 3.1% | |
| 0.0 | 8.2 | |
| over 1 year ago | 8 days ago | |
| Python | Python | |
| MIT License | MIT License |
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CausalWorld
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?
CARL - Benchmarking RL generalization in an interpretable way.
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.
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
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]
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
OmniIsaacGymEnvs-DofbotReacher - Dofbot Reacher Reinforcement Learning Sim2Real Environment for Omniverse Isaac Gym/Sim
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
OmniIsaacGymEnvs-UR10Reacher - UR10 Reacher Reinforcement Learning Sim2Real Environment for Omniverse Isaac Gym/Sim
tianshou - An elegant PyTorch deep reinforcement learning library.
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥