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All Projects → iffiX → Machin

iffiX / Machin

Licence: mit
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningpytorchreinforcement-learningdistributeddqnppoa3cddpg

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Readable, Reusable, Extendable

Machin is a reinforcement library designed for pytorch.


Supported Models

Anything, including recurrent networks.

Supported algorithms

Currently Machin has implemented the following algorithms, the list is still growing:

Single agent algorithms:

Multi-agent algorithms:

Massively parallel algorithms:

Enhancements:

Algorithms to be supported:

Features

1. Readable

Compared to other reinforcement learning libraries such as the famous rlpyt, ray, and baselines. Machin tries to just provide a simple, clear implementation of RL algorithms.

All algorithms in Machin are designed with minimial abstractions and have very detailed documents, as well as various helpful tutorials.

2. Reusable

Machin takes a similar approach to that of pytorch, encasulating algorithms, data structures in their own classes. Users do not need to setup a series of data collectors, trainers, runners, samplers... to use them, just import.

The only restriction placed on your models is their input / output format, however, these restrictions are minimal, making it easy to adapt algorithms to your custom environments.

3. Extendable

Machin is built upon pytorch, it and thanks to its powerful rpc api, we may construct complex distributed programs. Machin provides implementations for enhanced parallel execution pools, automatic model assignment, role based rpc scaling, rpc service discovery and registration, etc.

Upon these core functions, Machin is able to provide tested high-performance distributed training algorithm implementations, such as A3C, APEX, IMPALA, to ease your design.

4. Reproducible

Machin is weakly reproducible, for each release, our test framework will directly train every RL framework, if any framework cannot reach the target score, the test will fail directly.

However, currently, the tests are not guaranteed to be exactly the same as the tests in original papers, due to the large variety of different environments used in original research papers.

Documentation

See here. Examples are located in examples.

Installation

Machin is hosted on PyPI. Python >= 3.5 and PyTorch >= 1.5.0 is required. You may install the Machin library by simply typing:

pip install machin

You are suggested to create a virtual environment first if you are using conda to manage your environments, to prevent PIP changes your packages without letting conda know.

conda create -n some_env pip
conda activate some_env
pip install machin

Contributing

Any contribution would be great, don't hesitate to submit a PR request to us! Please follow the instructions in this file.

Issues

If you have any issues, please use the template markdown files in .github/ISSUE_TEMPLATE folder and format your issue before opening a new one. We would try our best to respond to your feature requests and problems.

Citing

We would be very grateful if you can cite our work in your publications:

@misc{machin,
  author = {Muhan Li},
  title = {Machin},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/iffiX/machin}},
}

Roadmap

Please see Roadmap for the exciting new features we are currently working on!

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].