Data and code underlying the master thesis: Using Reinforcement Learning to Personalize Daily Step Goals for a Collaborative Dialogue with a Virtual Coach

DOI:10.4121/6f8e6750-7494-4226-b6f9-299a9edbb077.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/6f8e6750-7494-4226-b6f9-299a9edbb077
Datacite citation style:
Dierikx, Martin; Albers, Nele; Brinkman, Willem-Paul (2023): Data and code underlying the master thesis: Using Reinforcement Learning to Personalize Daily Step Goals for a Collaborative Dialogue with a Virtual Coach. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/6f8e6750-7494-4226-b6f9-299a9edbb077.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains the data and analysis code from the observational study conducted for the thesis: Using Reinforcement Learning to Personalize Daily Step Goals for a Collaborative Dialogue with a Virtual Coach. In this thesis, we studied the use of reinforcement learning to personalize daily step goal proposals based on people's personal factors, such as mood and self-motivation. To train the reinforcement learning model, we ran an observational study with a virtual coach to gather data on people's personal factors. We then ran analyses on simulations using the collected data to investigate the effectiveness of the model.

History

  • 2023-10-02 first online, published, posted

Publisher

4TU.ResearchData

Format

.zip, .csv, .md, .pdf, .ipynb, .py, .png, .html

Funding

  • Part of Perfect Fit, funded by NWO, project number 628.011.211

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Interactive Intelligence Group