Subgoal identification for reinforcement learning and planning in multiagent problem solving

Chung Cheng Chiu*, Von Wun Soo

*Corresponding author for this work

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

3 Scopus citations

Abstract

We provide a new probability flow analysis algorithm to automatically identify subgoals in a problem space. Our flow analysis, inspired by preflow-push algorithms, measures the topological structure of the problem space to identify states that connect different subset of state space as the subgoals within linear-time complexity. Then we apply a hybrid approach known as subgoal-based SMDP (semi-Markov Decision Process) that is composed of reinforcement learning and planning based on the identified subgoals to solve the problem in a multiagent environment. The effectiveness of this new method used in a multiagent system is demonstrated and evaluated using a capture-the-flag scenario. We showed also that the cooperative coordination emerged between two agents in the scenario through distributed policy learning.

Original languageEnglish
Title of host publicationMultiagent System Technologies - 5th German Conference, MATES 2007, Proceedings
PublisherSpringer Verlag
Pages37-48
Number of pages12
ISBN (Print)9783540749486
DOIs
StatePublished - 2007
Externally publishedYes
Event5th German Conference on Multi-Agent System Technologies, MATES 2007 - Leipzig, Germany
Duration: 24 09 200726 09 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4687 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th German Conference on Multi-Agent System Technologies, MATES 2007
Country/TerritoryGermany
CityLeipzig
Period24/09/0726/09/07

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