Markov games are a generalization of Markov decision process to a multi-agent setting. Two-player zero-sum Markov game framework offers an effective platform for designing robust controllers. This paper presents two novel controller design algorithms that use ideas from game-theory literature to produce reliable controllers that are able to maintain performance in presence of noise and parameter variations. A more widely used approach for controller design is the H∞ optimal control, which suffers from high computational demand and at times, may be infeasible. Our approach generates an optimal control policy for the agent (controller) via a simple Linear Program enabling the controller to learn about the unknown environment. The controller is facing an unknown environment, and in our formulation this environment corresponds to the behavior rules of the noise modeled as the opponent. Proposed controller architectures attempt to improve controller reliability by a gradual mixing of algorithmic approaches drawn from the game theory literature and the Minimax-Q Markov game solution approach, in a reinforcement-learning framework. We test the proposed algorithms on a simulated Inverted Pendulum Swing-up task and compare its performance against standard Q learning.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Markov Game Controller Design Algorithms


    Beteiligte:
    Rajneesh Sharma (Autor:in) / M. Gopal (Autor:in)

    Erscheinungsdatum :

    29.10.2007


    Anmerkungen:

    oai:zenodo.org:1071626



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629 / 006




    Hybrid Markov Game Controller Design Algorithms for Nonlinear Systems

    R. Sharma / M. Gopal | BASE | 2007

    Freier Zugriff



    Optimal preview game theory approach to vehicle stability controller design

    Tamaddoni, Seyed Hossein / Taheri, Saied / Ahmadian, Mehdi | Taylor & Francis Verlag | 2011


    Optimal preview game theory approach to vehicle stability controller design

    Tamaddoni,S.H. / Taheri,S. / Ahmadian,M. et al. | Kraftfahrwesen | 2011