lagom is a light PyTorch infrastructure to quickly prototype reinforcement learning algorithms. Lagom is a 'magic' word in Swedish, "inte för mycket och inte för lite, enkelhet är bäst", meaning "not too much and not too little, simplicity is often the best". lagom is the philosophy on which this library was designed.

Contents of this document

• Basics
• Installation
  • Install dependencies
  • Install lagom
• Getting Started
• Documentation
• Examples
• Test
• Roadmap
• Reference

lagom balances between the flexibility and the usability when developing reinforcement learning (RL) algorithms. The library is built on top of PyTorch and provides modular tools to quickly prototype RL algorithms. However, it does not go overboard, because too low level is often time consuming and prone to potential bugs, while too high level degrades the flexibility which makes it difficult to try out some crazy ideas fast.

We are continuously making lagom more 'self-contained' to set up and run experiments quickly. It internally supports base classes for multiprocessing (master-worker framework) for parallelization (e.g. experiments and evolution strategies). It also supports hyperparameter search by defining configurations either as grid search or random search.

A common pipeline to use lagom can be done as following:

1. Define environment and agent (mainly for RL)
2. Use runner to collect data (trajectories or segments) for agent
3. Define engine for training and evaluating the agent
4. Define algorithm
5. Define experiment and configurations

A graphical illustration is coming soon.

Run the following command to install all required dependencies:

pip install -r requirements.txt

Note that it is highly recommanded to use an Anaconda environment:

conda create -n lagom python=3.7

We also provide some bash scripts in scripts/ directory to automatically set up the conda environment and dependencies.

Run the following commands to install lagom from source:

git clone https://github.com/zuoxingdong/lagom.git
cd lagom
pip install -e .

Installing from source allows to flexibly modify and adapt the code as you pleased, this is very convenient for research purpose which often needs fast prototyping.

Detailed tutorials is coming soon. For now, it is recommended to have a look in examples/ or the source code.

The documentation hosted by ReadTheDocs is available online at http://lagom.readthedocs.io

We are continuously providing examples/ to use lagom.

We are using pytest for tests. Feel free to run via

pytest test -v

- Tutorials

- IMPALA
- TRPO/PPO
- NAF/GAE
- ACKTR
- DDPG
- ACER
- Q-Prop
- DQN: DQN/Rainbow
- ES: NES

- Monte Carlo Dropout/Concrete Dropout

- Support pip install
- Technical report

This repo is inspired by OpenAI Gym, garage, OpenAI baselines and Dopamine

Please use this bibtex if you want to cite this repository in your publications:

@misc{lagom,
      author = {Zuo, Xingdong},
      title = {lagom: A light PyTorch infrastructure to quickly prototype reinforcement learning algorithms},
      year = {2018},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/zuoxingdong/lagom}},
    }