stable-baselines3
sample-factory
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stable-baselines3 | sample-factory | |
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46 | 6 | |
7,704 | 721 | |
4.7% | - | |
8.2 | 8.1 | |
7 days ago | 23 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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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 -
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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?
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Is Stable Baselines 3 no longer compatible with PettingZoo?
I was able to get Stable Baselines 3 to work with gymnasium by following the details in this work-in-progress PR: https://github.com/DLR-RM/stable-baselines3/pull/780. I have not used PettingZoo, though.
- [OC] InteligĂȘncia Artificial jogando Mega Man!
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New to reinforcement learning.
I'd say this is a great path but I'd also look at the basic on-policy gradient actor critic methods like A2C and eventually PPO. Someone recommended SAC which also really good. There are tons of environments in the https://github.com/Farama-Foundation/PettingZoo as well if you want to mess with those. You can also check out stable baselines https://github.com/DLR-RM/stable-baselines3 which is pretty popular. If you want to get into the theory more I recommend reading the Sutton and Barto book on reinforcement learning.
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How to proceed further? (Learning RL)
If you want to iterate quickly through different RL methods then it's a good idea to use one of the RL libraries like stable baselines 3. Then you can dig further into the methods that work best for you. Coding RL methods from scratch is very time consuming and error prone even for experienced programmers.
sample-factory
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A minimal RL library for infinite horizon tasks
I take a lot of inspiration from Sample Factory and RLlib for my own RL library's implementation. Although I thoroughly enjoy both of these libraries, they just didn't quite fit right with my use case which motivated me to start my own. Hopefully someone finds use in rlstack whether it be through direct usage or as inspiration for their own personalized library
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Fast and hackable frameworks for RL research
I'm tired of having my 200m frames of Atari take 5 days to run with dopamine, so I'm looking for another framework to use. I haven't been able to find one that's fast and hackable, preferably distributed or with vectorized environments. Anybody have suggestions? seed-rl seems promising but is archived (and in TF2). sample-factory seems super fast but to the best of my knowledge doesn't work with replay buffers. I've been trying to get acme working but documentation is sparse and many of the features are broken.
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How is IMPALA as a framework?
Sample Factory: https://github.com/alex-petrenko/sample-factory
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Best PyTorch RL library for doing research
I borrow a lot of performance tricks from sample factory, which is awesome but hard to modify from its original APPO algorithm. rlpyt was more modular, and I borrowed more ideas from it (namedarraytuple), but still too limited.
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.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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. đ„
SuperSuit - A collection of wrappers for Gymnasium and PettingZoo environments (being merged into gymnasium.wrappers and pettingzoo.wrappers
Tic-Tac-Toe-Gym - This is the Tic-Tac-Toe game made with Python using the PyGame library and the Gym library to implement the AI with Reinforcement Learning
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.