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 language | English |
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Title of host publication | Proceedings Robotics: Science and System XVIII |
Editors | Kris Hauser, Dylan Shell, Shoudong Huang |
Publisher | Robotics Science and Systems (RSS) |
Number of pages | 12 |
ISBN (Electronic) | 978-0-9923747-8-5 |
ISBN (Print) | 9780992374785 |
DOIs | |
Publication status | Published - 2022 |
Event | Robotics: Science and Systems 2022 - New York, United States Duration: 27 Jun 2022 → 1 Jul 2022 |
Publication series
Name | Robotics: Science and Systems |
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ISSN (Electronic) | 2330-765X |
Conference
Conference | Robotics: Science and Systems 2022 |
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Country/Territory | United States |
City | New York |
Period | 27/06/22 → 1/07/22 |
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).