Krzysztof Krawiec


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In this paper we apply Coevolutionary Temporal Difference Learning (CTDL), a hybrid of coevolutionary search and reinforcement learning proposed in our former study, to evolve strategies for playing the game of Go on small boards (5×5). CTDL works by interlacing exploration of the search space provided by one-population competitive coevolution and exploitation by means of temporal difference learning. Despite using simple representation of strategies (weighted piece counter), CTDL proves able to evolve players that defeat solutions found by its constituent methods. The results of the conducted experiments indicate that our algorithm turns out to be superior to pure coevolution and pure temporal difference learning, both in terms of performance of the elaborated strategies and the computational cost. This demonstrates the existence of synergistic interplay between components of CTDL, which we also briefly discuss in this study. #2010CECKrawiecSzubertBib

@INPROCEEDINGS { 2010CECKrawiecSzubert,
    AUTHOR = { Krzysztof Krawiec and Marcin Szubert },
    TITLE = { Coevolutionary Temporal Difference Learning for small-board Go },
    COMMENT = { ProjectELP },
    EE = { http://dx.doi.org/10.1109/CEC.2010.5586054 },
    BOOKTITLE = { Evolutionary Computation (CEC), 2010 IEEE Congress on },
    YEAR = { 2010 },
    PAGES = { 1-8 },
    ABSTRACT = { In this paper we apply Coevolutionary Temporal Difference Learning (CTDL), a hybrid of coevolutionary search and reinforcement learning proposed in our former study, to evolve strategies for playing the game of Go on small boards (5×5). CTDL works by interlacing exploration of the search space provided by one-population competitive coevolution and exploitation by means of temporal difference learning. Despite using simple representation of strategies (weighted piece counter), CTDL proves able to evolve players that defeat solutions found by its constituent methods. The results of the conducted experiments indicate that our algorithm turns out to be superior to pure coevolution and pure temporal difference learning, both in terms of performance of the elaborated strategies and the computational cost. This demonstrates the existence of synergistic interplay between components of CTDL, which we also briefly discuss in this study. },
    KEYWORDS = { evolutionary computation;game theory;learning (artificial intelligence);coevolutionary search;coevolutionary temporal difference learning;one-population competitive coevolution;reinforcement learning;small-board go;Artificial neural networks;Computers;Evolutionary computation;Games;Genetics;Humans;Learning },
    DOI = { 10.1109/CEC.2010.5586054 },
    MONTH = { July },
}


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