Welcome to energym!

See also

This is a project based on Zhiang Zhang and Khee Poh Lam Gym-Eplus.

The goal of energym is to create an environment following OpenAI Gym interface for wrapping simulation engines for building control using deep reinforcement learning.

Energym diagram

The main functionalities of energym are the following:

  • Benchmark environments. Similarly to Atari or Mujoco environments for RL community, we are designing a set of environments for benchmarking and testing deep RL algorithms. These environments may include different buildings, weathers or action spaces.

  • Develop different experimental settings. We aim to provide a package that allows to modify experimental settings in an easy manner. For example, several reward functions or observation variables may be defined.

  • Include different simulation engines. Communication between Python and EnergyPlus is established using BCVTB. Since this tool allows for interacting with several simulation engines, more of them (e.g. OpenModelica) could be included in the backend while maintaining the Gym API.

  • Stable Baseline 3 Integration. Some functionalities like callbacks have been developed by our team in order to test easily these environments with deep reinforcement learning algorithms.

  • Many more!

Note

This is a work in progress project. Stay tuned for upcoming releases!