Learning Mixed Strategies in Trajectory Games

L. Peters, David Fridovich-Keil, L. Ferranti, Cyrill Stachniss, Javier Alonso-Mora, Forrest Laine

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another’s behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional “predict then plan” approaches. However, current game-theoretic planning methods have important limitations. In this work, we propose two main contributions. First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games. Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive “mixed” strategies. We validate our approach on a number of experiments using the pursuit-evasion game “tag.”
Original languageEnglish
Title of host publicationProceedings Robotics: Science and System XVIII
EditorsKris Hauser, Dylan Shell, Shoudong Huang
PublisherRobotics Science and Systems (RSS)
Number of pages12
ISBN (Electronic)978-0-9923747-8-5
ISBN (Print)9780992374785
DOIs
Publication statusPublished - 2022
EventRobotics: Science and Systems 2022 - New York, United States
Duration: 27 Jun 20221 Jul 2022

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X

Conference

ConferenceRobotics: Science and Systems 2022
Country/TerritoryUnited States
CityNew York
Period27/06/221/07/22

Bibliographical note

Funding Information:
This work was supported in part by the National Police of the Netherlands. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors. L. Ferranti received support from the Dutch Science Foundation NWOTTW within the Veni project HARMONIA (nr. 18165).

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