Beobench docs#

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A toolkit providing easy and unified access to building control environments for reinforcement learning (RL). Compared to other domains, RL environments for building control tend to be more difficult to install and handle. Most environments require the user to either manually install a building simulator (e.g. EnergyPlus) or to manually manage Docker containers. This can be tedious.

Beobench was created to make building control environments easier to use and experiments more reproducible. Beobench uses Docker to manage all environment dependencies in the background so that the user doesn’t have to. A standardised API, illustrated in the figure below, allows the user to easily configure experiments and evaluate new RL agents on building control environments.

Beobench

Features#

  • Large collection of building control environments: Out-of-the-box Beobench provides access to environments from BOPTEST, Energym, and Sinergym. Beobench combines the environments from these frameworks into the (to the best of our knowledge) largest single collection of building control environments. See environment list here.

  • Clean and light-weight installation: Beobench is installed via pip and only requires Docker as an additional non-python dependency (see installation guide). Without Beobench, most building control environments will require manually installing building simulators or directly managing docker containers.

  • Built-in RL agents: Beobench allows the user to apply any agent from the Ray RLlib collection in addition to agents provided by the user directly.

  • Easily extendable: want to use Beobench with an environment not yet included? The support for user-defined Docker contexts makes it easy to use Beobench with any RL environment.