/EHCSTLDRL

Enhancing HVAC Control Systems through Transfer Learning with Deep Reinforcement Learning Agents

Primary LanguagePythonMIT LicenseMIT

Enhancing HVAC Control Systems through Transfer Learning with Deep Reinforcement Learning Agents

Link to Research - https://www.sciencedirect.com/science/article/pii/S2666955224000017

Installation

In a conda environment, run the following code.

git clone https://github.com/kad99kev/EHCSTLDRL.git
pip install -e .

Running an experiment.

Before running an experiment, the Docker environment needs to be built first. This can be done by running:

ehcs build

Once the Docker container is built, there are different options available:

  1. controller - Will run an experiment using a rule-based controller agent.
  2. train - Will train a Deep RL agent.
  3. test- Will test a trained Deep RL agent.

The commands can be run as follows:

ehcs command_name -c path/to/config

Sample configuration files for PPO and SAC are given in configs/

Experiment tracking with Weights and Biases is supported. Enter the information required in a wandb section of the configuration file to enable experiment tracking.