Leveraging human knowledge in tabular Reinforcement learning: A study of human subjects

Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus

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

Abstract

Reinforcement Learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort on the human designer's part. To date, human factors are generally not considered in the development and evaluation of possible approaches. In this paper, we propose and evaluate a novel method, based on human psychology literature, which we show to be both effective and efficient, for both expert and non-expert designers, in injecting human knowledge for speeding up tabular RL.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
Pages3823-3830
Number of pages8
ISBN (Electronic)9780999241103
DOIs
StatePublished - 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume0

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Country/TerritoryAustralia
CityMelbourne
Period19/08/1725/08/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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