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.
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
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
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
Add integration with sinergym
Move gym integrations to separate beobench_contrib repo
Make usage of GPUs in containers optional
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
First release on PyPI.
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