Machin is a reinforcement library designed for pytorch.

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


1. Automatic

Starting from version 0.4.0, Machin now supports automatic config generation, you can get a configuration

python -m generate --algo DQN --env openai_gym --output config.json

And automatically launch the experiment with pytorch lightning:

python -m launch --config config.json

2. 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.

3. 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.

4. 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.

5. 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.


See here. Examples are located in examples.


Machin is hosted on PyPI. Python >= 3.6 and PyTorch >= 1.6.0 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

Note: Currently only a fraction of all functions is supported on Windows, to test whether
the code is running correctly, you can run the corresponding test script in the root directory: