stable-baselines3
stable-baselines3-contrib
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stable-baselines3 | stable-baselines3-contrib | |
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46 | 6 | |
7,894 | 427 | |
5.2% | 8.0% | |
8.2 | 6.7 | |
7 days ago | 28 days ago | |
Python | Python | |
MIT License | MIT License |
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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?
stable-baselines3-contrib
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Problem with Truncated Quantile Critics (TQC) and n-step learning algorithm.
# https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/blob/master/sb3_contrib/tqc/tqc.py :
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Understanding Action Masking in RLlib
Here's a theoretical overview and an implementation of action masking for PPO.
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PPO rollout buffer for turn-based two-player game with varying turn lengths
Simplified version of rollout collection (adapted from ppo_mask.py line 282):
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GitHub Copilot: your AI pair programmer
Transformers (GPT-3) aren't quite _supervised_, but it does require valid samples.
Agree 100% with RL being the path forward. You probably have already seen ( https://venturebeat.com/2021/06/09/deepmind-says-reinforceme... ). Personally I'm really stoked for this https://github.com/Stable-Baselines-Team/stable-baselines3-c... , which will make it a lot easier for rubes like me to use RL.
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[P] Stable-Baselines3 v1.0 - Reliable implementations of RL algorithms
But as we already have vanilla DQN and QR-DQN (in our contrib repo: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ) I think it is already a good start for off-policy discrete action algorithms. (QR-DQN is usually competitive vs DQN+extensions)
What are some alternatives?
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.
muzero-general - MuZero
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
TabNine - AI Code Completions
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
stable-baselines3-c
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
copilot-cli - The AWS Copilot CLI is a tool for developers to build, release and operate production ready containerized applications on AWS App Runner or Amazon ECS on AWS Fargate.
tianshou - An elegant PyTorch deep reinforcement learning library.
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
dreamerv2 - Mastering Atari with Discrete World Models