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Beobench is a toolbox for benchmarking reinforcement learning (RL) algorithms on building energy optimisation (BEO) problems.

Project description

Beobench https://img.shields.io/pypi/v/beobench.svg Documentation Status License

A toolbox for benchmarking reinforcement learning (RL) algorithms on building energy optimisation (BEO) problems. Beobench tries to make working on RL for BEO easier: it provides simple access to existing libraries defining BEO problems (such as BOPTEST) and provides a large set of pre-configured RL algorithms. Beobench is not a gym library itself - instead it leverages the brilliant work done by many existing gym-type projects and makes their work more easily accessible.

Features

Some of the features are work in progress

Main features

  • RL algorithm collection: what’s the best RL method for your BEO problem? Building on Ray RLlib, beobench provides a large collection of pre-configured RL algorithm experiments that can be easily applied to your new BEO problem.

  • Problem collection: beobench provides ready-to-use docker containers for popular BEO gym-type problem libraries. By enforcing a strict OpenAI gym.Env it makes testing your method on different libraries easy.

Additional features

  • Experiment logging: log experiment results in a reproducible and shareable manner via Weights and Biases.

  • Hyperparameter tuning: easily tune hyperparameters using the extensive Ray Tune Search API.

  • Simple installation: beobench can be installed via pip and only requires docker as an additional non-python dependency.

  • Easily extendable: beobench is designed for the user to add both environments and methods.

Quickstart

Run your first beobench experiment in three steps:

  1. Install docker on your machine (if on Linux, check the additional installation steps)

  2. Install beobench using:

    pip install beobench
  3. Finally, start your first experiment using:

    python -m beobench.experiment.scheduler

Done, you have just started your first experiment… congrats! Check out the full getting started guide in the documentation for the next steps.

Documentation

https://beobench.readthedocs.io

License

MIT license

Credits

This package was originally created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.2.0 (2022-01-18)

  • Enable adding custom environments to beobench with docker build context-based syntax

  • Save experiment results on host machine

  • Major improvements to documentation

  • Remove unnecessary wandb arguments in main CLI

0.1.0 (2022-01-10)

  • First release on PyPI.

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