Date of Award
Spring 2019
Degree Type
Masters Thesis
Degree Name
Master of Science (MS)
School
Computing Sciences and Computer Engineering
Committee Chair
Bikramjit Banerjee
Committee Chair School
Computing Sciences and Computer Engineering
Committee Member 2
Beddhu Murali
Committee Member 2 School
Computing Sciences and Computer Engineering
Committee Member 3
Dia Ali
Committee Member 3 School
Computing Sciences and Computer Engineering
Abstract
Among an array of techniques proposed to speed-up reinforcement learning (RL), learn- ing from human demonstration has a proven record of success. A related technique, called Human Agent Transfer (HAT), and its confidence-based derivatives have been successfully applied to single agent RL. This paper investigates their application to collaborative multi- agent RL problems. We show that a first-cut extension may leave room for improvement in some domains, and propose a new algorithm called coordination confidence (CC). CC analyzes the difference in perspectives between a human demonstrator (global view) and the learning agents (local view), and informs the agents’ action choices when the difference is critical and simply following the human demonstration can lead to miscoordination. We conduct experiments in three domains to investigate the performance of CC in comparison with relevant baselines.
Copyright
2019, Syamala Nanditha Vittanala
Recommended Citation
Vittanala, Syamala Nanditha, "Team Learning from Human Demonstration with Coordination Confidence" (2019). Master's Theses. 629.
https://aquila.usm.edu/masters_theses/629