Learning Spatial Search using Submodular Inverse Reinforcement Learning

Ji Jie Wu, Kuo Shih Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Finding an optimal search path is a NP-hard problem. Since search is one of human central activities, learning spatial search behavior from human operators is a way to solve search problems. Utilizing the submodularity of search problems, this research proposes a submodular inverse reinforcement learning (SIRL) algorithm to learn humans' search behavior. The experiments demonstrate that the performance of the learned search paths outperform that of state of the art approaches (e.g., MaxEnt IRL and DIRL).

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020
EditorsLino Marques, Majid Khonji, Jorge Dias
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-14
Number of pages8
ISBN (Electronic)9781665403900
DOIs
StatePublished - 4 Nov 2020
Event2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020 - Abu Dhabi, United Arab Emirates
Duration: 4 Nov 20206 Nov 2020

Publication series

Name2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020

Conference

Conference2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period4/11/206/11/20

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