This release marks the initial implementation of the repository focused on Deep Reinforcement Learning (DRL) integration with EnergyPlus Python API (v23.2.0) and RLlib by Ray (v2.9.1). The primary objective of this repository is to provide a platform for experimenting with DRL techniques in conjunction with natural ventilation strategies for residential buildings. Key features of this release include:
* Setup of the project structure and dependencies.
* Integration of EnergyPlus Python API (v23.2.0) for building simulations.
* Implementation of RLlib by Ray (v2.9.1) for Deep Reinforcement Learning experiments.
* Inclusion of initial scripts and configurations for experimenting with natural ventilation strategies.
This release lays the foundation for further development and exploration in the field of bioclimatic design, renewable energy, and optimization of residential building energy systems through DRL methodologies.