beobench 0.4.0

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.

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

History

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.