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Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains [in press]

Köpf, Florian; Nitsch, Alexander; Flad, Michael; Hohmann, Sören

Abstract:
Mixed cooperative-competitive control scenarios where the interacting partners exhibit individual goals are very challenging for reinforcement learning agents. An example of such scenarios is given by human-machine interaction. In order to contribute towards intuitive human-machine collaboration, this work focuses on problems in the continuous state and control domain and prohibits explicit communication. More precisely, the agents do not know the others' goals or control laws but only sense their control inputs retrospectively. The proposed framework combines a partner model learned from online data with a reinforcement learning agent that is trained in a simulated environment including the partner model. This procedure overcomes drawbacks of independent learners and benefits from a reduced amount of real world data required for reinforcement learning---an aspect that is vital in the human-machine context.
Experimental results reveal that the method learns fast due to the simulated environment and adapts to the constantly changing partner due of the partner model.



Zugehörige Institution(en) am KIT Institut für Regelungs- und Steuerungssysteme (IRS)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator KITopen-ID: 1000117900
Erschienen in ICCAR 2020
Veranstaltung 6th International Conference on Control, Automation and Robotics (ICCAR 2020), Singapur, Singapur, 20.04.2020 – 23.04.2020
Verlag IEEE
Schlagwörter Reinforcement Learning, Mixed Cooperative-Competitive Control, Machine Learning in Control, Opponent Modeling
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