Reinforcement Learning with Guarantees that Hold for Ever

Ernst Moritz Hahn, Mateo Perez, Sven Schewe*, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

*Corresponding author for this work

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

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Reinforcement learning is a successful explore-and-exploit approach, where a controller tries to learn how to navigate an unknown environment. The principle approach is for an intelligent agent to learn how to maximise expected rewards. But what happens if the objective refers to non-terminating systems? We can obviously not wait until an infinite amount of time has passed, assess the success, and update. But what can we do? This talk will tell.
Original languageEnglish
Title of host publicationFormal Methods for Industrial Critical Systems
Subtitle of host publication27th International Conference, FMICS 2022, Warsaw, Poland, September 14-15, 2022, Proceedings
EditorsJan Friso Groote, Marieke Huisman
Place of PublicationCham
Number of pages5
ISBN (Electronic)978-3-031-15008-1
ISBN (Print)978-3-031-15007-4
Publication statusPublished - 5 Sept 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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