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Decentralized MCTS via Learned Teammate Models

Aleksander Czechowski and Frans A. Oliehoek. Decentralized MCTS via Learned Teammate Models. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI), pp. 81–88, July 2020.

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Abstract

Decentralized online planning can be an attractive paradigm for cooperative multi-agent systems, due to improved scalability and robustness. A key diffi- culty of such approach lies in making accurate pre- dictions about the decisions of other agents. In this paper, we present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search, combined with models of team- mates learned from previous episodic runs. By only allowing one agent to adapt its models at a time, un- der the assumption of ideal policy approximation, successive iterations of our method are guaranteed to improve joint policies, and eventually lead to convergence to a Nash equilibrium. We test the effi- ciency of the algorithm by performing experiments in several scenarios of the spatial task allocation en- vironment introduced in [Claes et al., 2015]. We show that deep learning and convolutional neural networks can be employed to produce accurate pol- icy approximators which exploit the spatial features of the problem, and that the proposed algorithm im- proves over the baseline planning performance for particularly challenging domain configurations.

BibTeX Entry

@inproceedings{Czechowski20IJCAI,
       author = {{Czechowski}, Aleksander and {Oliehoek}, Frans A.},
        title = {Decentralized {MCTS} via Learned Teammate Models},
     booktitle= IJCAI20,
         year = 2020,
        month = jul,
        pages = {81--88},         
          doi = {10.24963/ijcai.2020/12},
          url = {https://doi.org/10.24963/ijcai.2020/12},
    keywords =   {refereed},
    abstract =  {    
    Decentralized online planning can be an attractive
    paradigm for cooperative multi-agent systems, due
    to improved scalability and robustness. A key diffi-
    culty of such approach lies in making accurate pre-
    dictions about the decisions of other agents. In this
    paper, we present a trainable online decentralized
    planning algorithm based on decentralized Monte
    Carlo Tree Search, combined with models of team-
    mates learned from previous episodic runs. By only
    allowing one agent to adapt its models at a time, un-
    der the assumption of ideal policy approximation,
    successive iterations of our method are guaranteed
    to improve joint policies, and eventually lead to
    convergence to a Nash equilibrium. We test the effi-
    ciency of the algorithm by performing experiments
    in several scenarios of the spatial task allocation en-
    vironment introduced in [Claes et al., 2015]. We
    show that deep learning and convolutional neural
    networks can be employed to produce accurate pol-
    icy approximators which exploit the spatial features
    of the problem, and that the proposed algorithm im-
    proves over the baseline planning performance for
    particularly challenging domain configurations.
    }
}

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