A case study on improving defense behavior in soccer simulation 2D: The neurohassle approach
Autor(en): | Gabel, T. Riedmiller, M. Trost, F. |
Stichwörter: | Defense strategy; Defensive strategies; RoboCup; Soccer simulation, Reinforcement | Erscheinungsdatum: | 2009 | Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Volumen: | 5399 LNAI | Startseite: | 61 | Seitenende: | 72 | Zusammenfassung: | While a lot of papers on RoboCup's robotic 2D soccer simulation have focused on the players' offensive behavior, there are only a few papers that specifically address a team's defense strategy. In this paper, we consider a defense scenario of crucial importance: We focus on situations where one of our players must interfere and disturb an opponent ball leading player in order to scotch the opponent team's attack at an early stage and, even better, to eventually conquer the ball initiating a counter attack. We employ a reinforcement learning methodology that enables our players to autonomously acquire such an aggressive duel behavior, and we have embedded it into our soccer simulation team's defensive strategy. Employing the learned NeuroHassle policy in our competition team, we were able to clearly improve the capabilities of our defense and, thus, to increase the performance of our team as a whole. © 2009 Springer Berlin Heidelberg. |
Beschreibung: | Conference of 12th annual RoboCup International Symposium, RoboCup 2008 ; Conference Date: 15 July 2008 Through 18 July 2008; Conference Code:77265 |
ISBN: | 9783642029202 | ISSN: | 03029743 | DOI: | 10.1007/978-3-642-02921-9_6 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349314519&doi=10.1007%2f978-3-642-02921-9_6&partnerID=40&md5=665303790b0c4ee83bd63385b02d3169 |
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