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  • 1.
    Girard, Justin
    et al.
    Institute for Aerospace Studies, University of Toronto.
    Emami, Reza
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    A robust approach to robot team learning2016In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 40, no 8, p. 1441-1457Article in journal (Refereed)
    Abstract [en]

    The paper achieves two outcomes. First, it summarizes previous work on concurrent Markov decision processes (CMDPs) currently demonstrated for use with multi-agent foraging problems. When using CMDPs, each agent models the environment using two Markov decision process (MDP). The two MDPs characterize a multi-agent foraging problem by modeling both a single-agent foraging problem, and multi-agent task allocation problem, for each agent. Second, the paper studies the effects of state uncertainty on a heterogeneous robot team that utilizes the aforementioned CMDP modelling approach. Furthermore, the paper presents a method to maintain performance despite state uncertainty. The resulting robust concurrent individual and social learning (RCISL) mechanism leads to an enhanced team learning behaviour despite state uncertainty. The paper analyzes the performance of the concurrent individual and social learning mechanism with and without a particle filter for a heterogeneous foraging scenario. The RCISL mechanism confers statistically significant performance improvements over the CISL mechanism

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