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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.


0.4.4 (2022-05-09)

  • Features:

    • Add general support for wrappers. (#28)

  • Improvements:

    • Make dev beobench build part of image build process for improved speed.

    • Add number of environment steps (env_step) to wandb logging.

    • Update logo to new version (#48)

    • Update docs and main readme to include more useful quickstart guide, which includes a custom agent (#47)

  • Fixes:

    • Enable automatic episode data logging in RLlib integration for long training periods.

    • Update broken links in main readme env list (#40)

0.4.3 (2022-04-12)

  • Feature: enable easy access to standard configs via util method

  • Feature: add non-normalised observations to info in energym integration (#62)

  • Feature: enable logging full episode data from RLlib and adding this data to wandb (#62)

  • Feature: ship integrations with package improving image build times (#44)

  • Feature: add wandb logging support for random agent script (#59)

  • Feature: add rule-based agent script based on energym controller (#60)

  • Fix: add importlib-resources backport package to requirements

  • Fix: allow users to disable reset() method in energym envs (#43)

  • Aux: add automatic deployment of PyPI package via GitHub actions (#50)

  • Aux: add tests and automatic checks on PRs (#25)

0.4.2 (2022-04-04)

  • Feature: defining all relevant options/kwargs of CLI an API is now supported yaml files (#54)

  • Feature: allow multiple configs to be given to both CLI (giving multiple -c options) and Python API (as a list) (#51)

  • Fix: adapted Energym env reset() method to avoid triggering long warm-up times with additional simulation runs (#43)

  • Fix: enable container build even if prior build failed midway and left artifacts

0.4.1 (2022-03-30)

  • Feature: enable package extras to be given in development mode

  • Feature: add support for arm64/aarch64-based development by forcing experiment containers to run as amd64 containers on those systems (#32)

  • Fix: add gym to extended package requirements

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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