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Enhancing HVAC Control Efficiency: A Hybrid Approach Using Imitation and Reinforcement Learning

Link to Research - https://link.springer.com/chapter/10.1007/978-3-031-70378-2_16

Presentation Link - https://www.youtube.com/watch?v=esbOhlL9TSU

Setup

This repository runs in a Docker container configured by Sinergym.

Follow the instructions on how to install Sinergym via Docker and then follow the steps below.

Installation

In a conda or virtual environment, run the following code.

git clone <this_repo_url>
pip install -e .

Running an experiment.

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

  1. controller - Will run an experiment using a rule-based controller agent.
  2. imitate - Will train an agent with imitation learning.
  3. scratch - Will train a Deep RL agent from scratch (no fine-tuning).
  4. finetune - Will finetune a Deep RL agent using pre-trained weights.
  5. test- Will test any agent (trained via imitate, scratch or finetune).

The commands can be run as follows:

hvacirl scratch -c path/to/config -s 0

Run hvacirl --help for more information.

Example configuration file is given in example_cfg.yaml.

Dataset generation.

To generate the dataset used for pre-training, run the data_collector.ipynb Jupyter Notebook. This will generate .csv files that can then be used for pre-training.