Beobench is a toolbox for benchmarking reinforcement learning (RL) algorithms on building energy optimisation (BEO) problems.
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.
Some of the features are work in progress
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.
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.
Run your first beobench experiment in three steps:
Install beobench using:
pip install beobench
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.
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
First release on PyPI.
Release history Release notifications | RSS feed
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