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.
Add complete redesign of CLI: main command changed from python -m beobench.experiment.scheduler to beobench run.
Add support for energym environments
Add support for MLflow experiment tracking
Add support for custom agents
Add integration with sinergym
Move gym integrations to separate beobench_contrib repo
Make usage of GPUs in containers optional
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.
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