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

Project description

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.

For more information go to the documentation and the GitHub code repository.


0.4.0 (2022-03-28)

  • Make dependencies that are only used inside experiment/gym containers optional (for all dependencies install via pip install beobench[extended])

  • Add two part experiment image build process so that there is shared beobench installation dockerfile

  • Add support for yaml config files (!)

  • Overhaul of documentation, including new envs page and new theme

  • Enable RLlib free experiment containers when not required

  • Add beobench_contrib as submodule

  • Simplify Pypi readme file

  • Remove GPU requirement for devcontainer

0.3.0 (2022-02-14)

  • 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

0.2.1 (2022-02-03)

  • Add integration with sinergym

  • Move gym integrations to separate beobench_contrib repo

  • Make usage of GPUs in containers optional

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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