Regular decision processes: Modelling dynamic systems without using hidden variables

Ronen I. Brafman, Giuseppe De Giacomo

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

Abstract

We describe Regular Decision Processes (RDPs) a model in between MDPs and POMDPs. Like in POMDPs, the effect of an action may depend on the entire history of actions and observations, but this dependence is restricted to regular functions only. This makes RDP a tractable, yet rich model, that does not hypothesize hidden state, and could possibly be useful for learning dynamic systems.

Original languageEnglish
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1844-1846
Number of pages3
ISBN (Electronic)9781510892002
StatePublished - 1 Jan 2019
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: 13 May 201917 May 2019

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Country/TerritoryCanada
CityMontreal
Period13/05/1917/05/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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