Abstract
In this thesis, we explore three machine-learning and game-theoretic inspired strategies to address the Freeze-Tag Problem with adversarial agents (FTPA). We develop a custom Gymnasium envi- ronment where unfrozen robots collaborate to “tag” and unfreeze immobile frozen robots while avoiding being tagged by the adversaries. The FTPA models scenarios such as restoring disabled relay towers after a cyberattack where different towers vary in priority, repair difficulty, and ad- versarial threats. We evaluate three strategies: a baseline heuristic where unfrozen robots target the nearest untargeted frozen robot, a hierarchical approach that incorporates adversary interac- tions and likelihood of success into decision-making, and a centralized multi-agent reinforcement learning strategy using Proximal Policy Optimization (PPO).
Library of Congress Subject Headings
Robots--Automatic control; Machine learning; Game theory
Publication Date
12-17-2024
Document Type
Thesis
Student Type
Graduate
Degree Name
Computer Science (MS)
Department, Program, or Center
Computer Science, Department of
College
Golisano College of Computing and Information Sciences
Advisor
Reynold Bailey
Advisor/Committee Member
Zachary Butler
Advisor/Committee Member
Joe Geigel
Recommended Citation
Giacovelli, Matthew, "Machine-Learning and Game-Theoretic Inspired Strategies for Adversarial Freeze-Tag" (2024). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11984
Campus
RIT – Main Campus
Plan Codes
COMPSCI-MS
