Project Description

Reward discounting is a key concept in Reinforcement Learning. It determines how much an agent cares about rewards in the distant future relative to those rewards of the immediate future. Current methods of reward discounting use an exponential discounting method that leads to a theoretical convergence of the Bellman equation. However, psychology, economics and neuroscience suggest that humans and animals instead have a hyperbolic time preference [read more]. This leads us to some of our current work where we experiment with non-exponential discounting methods in various areas of study such as generalization with on-policy and off-policy methods.

Keywords

Reinforcement Learning, Reward Discounting

Methods

a) Survival Analysis

TBD

b) Multi-Horizon Learning

TBD

Current Team Members

• Raja Farrukh Ali - Ph.D. student, Computer Science, Kansas State University - (Lead)
• Nasik Muhammad Nafi - Ph.D. student, Computer Science, Kansas State University
• Kevin Duong - Undergraduate, Computer Science, Kansas State University
• Mike Hulcy- Undergraduate, Computer Science, Kansas State University
• Trey Etzel - Undergraduate, Computer Science, Kansas State University

Affiliates

• None

Alumni

• Jacob Legg - Undergraduate, Computer Science, Kansas State University

Data Sets

TBD

Link to any data sets for the project here. For any federally-sponsored research project (especially NSF and NIH-sponsored projects) of the KDD Lab, there must be an open access data repository. These may be subdirectories of a Bitbucket repository, but link to them separately here anyway. For all other projects, link to sites where data produced from the project are shared - e.g., a publisher server, Kaggle landing page, NIST documentation page, etc. Link to any data sharing agreements here.

Trello Board

TBD Every KDD Lab project must have a Trello Team and Trello Board, which must be private. Link to the Trello Board for the project here.

Source Code

TBD Every KDD Lab project must have a Bitbucket repository, which may be public or private. Link to the repository or repositories for the project here.

References

TBD

Background and Related Work

TBD

See the Machine Learning and Probabilistic Reasoning subpages of the original KDD wiki (v1, 2001 - 2006).

KDD Lab Publications

TBD

Use APA citation format and make sure citations are synchronized with the pages listing conference papers, journal articles, book chapters, posters, and student publications.

• De La Torre, M. F., Aguirre, C. A., Anshutz, B., & Hsu, W. (2018). MATESC: Metadata-Analytic Text Extractor and Section Classifier for Scientific Publications. Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018): International Conference on Knowledge Discovery and Information Retrieval (KDIR 2018), Seville, Spain, September 18-20, 2018
• Yates, H., Chamberlain, B., Healey, J., & Hsu, W. (2018). Binary Classification of Arousal in Built Environments using Machine Learning. Working Notes of the 2nd International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Artificial Intelligence in Affective Computing, Stockholm, Sweden, July 15, 2018.

Last updated Thursday, Nov 26th, 2022