machin
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ... (by iffiX)
stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms. (by DLR-RM)
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| machin | stable-baselines3 | |
|---|---|---|
| 2 | 46 | |
| 381 | 7,894 | |
| - | 5.2% | |
| 1.8 | 8.2 | |
| over 2 years ago | 6 days ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
machin
Posts with mentions or reviews of machin.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-04-30.
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Best PyTorch RL library for doing research
Machin is really nice, it is very easy to use and to try different things, although it’s developed by one person and maybe not appropriately tested yet.
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Is there a consensus about RL frameworks?
I found this repo very helpful to get started: https://github.com/iffiX/machin
stable-baselines3
Posts with mentions or reviews of stable-baselines3.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-09.
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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?
When comparing machin and stable-baselines3 you can also consider the following projects:
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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.
Apache Impala - Apache Impala
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
tianshou - An elegant PyTorch deep reinforcement learning library.
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
machin vs cleanrl
stable-baselines3 vs Ray
machin vs Apache Impala
stable-baselines3 vs stable-baselines
machin vs RL-Adventure
stable-baselines3 vs Pytorch
machin vs tianshou
stable-baselines3 vs cleanrl
machin vs ElegantRL
stable-baselines3 vs tianshou
machin vs seed_rl
stable-baselines3 vs Super-mario-bros-PPO-pytorch