Practical overview
When it fits
Use Machin when a Python and PyTorch project needs reusable reinforcement-learning implementations, including options for multi-agent or parallel training.
Who it is for
- Python developers building reinforcement-learning agents with PyTorch.
- Researchers comparing implemented reinforcement-learning algorithms.
- Developers adapting supported algorithms to custom models or environments.
Documented uses
- Implement agents using algorithms such as DQN, PPO, SAC or MADDPG.
- Compare reinforcement-learning approaches in experiments.
- Use parallel-training implementations such as A3C, APEX and IMPALA.
- Adapt algorithms to custom models, including recurrent networks.
Requirements & limits
- Installation requires Python 3.6 or later and PyTorch 1.6.0 or later.
- Machin is available through pip and is licensed under MIT.
- The README identifies Linux and Windows, while warning that some functions—mainly distributed algorithms—have limited support on platforms other than Linux.
- Some setups may require additional libraries, with Graphviz given as an example.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Machin in a Python environment
Install the Machin library using the package manager and prerequisites stated in the project README.
Before you start
- Python 3.6 or later
- PyTorch 1.6.0 or later
- pip
Steps
Optionally create and activate a conda environment with pip.
Run the command below.
pip install machin
Expected outcome
The Machin library is installed through PyPI in the active Python environment.
Guide limitations
- Some functions, mainly distributed algorithms, have limited support on platforms other than Linux.
- Some errors may result from missing library setup; the README names Graphviz as an example.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- iffiX/machin · Official homepage title · retained in its source language

