Extension to OpenAI Gym interface for building energy optimisation allowing diverse controllers, including RL and MPC.
A toolbox for benchmarking reinforcement learning (RL) algorithms on building energy optimisation (BEO) problems. Beobench does not replace existing libraries defining BEO problems (such as BOPTEST) — instead it makes working with them easier.
Wide range of RL algorithms: test the most common RL algorithms on BEO problems without re-implementing by using beobench’s Ray RLlib integration.
Experiment logging: log experiment results in a reproducible and sharable manner via Weights and Biases.
Hyperparameter tuning: easily tune hyperparameters using the extensive Ray Tune syntax.
Installers: avoid having to manage messy Python namespaces yourself — just install beobench via pip and use its pre-configured docker containers to take care of managing other BEO packages and their dependencies.
First release on PyPI.
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