Coevolutionary Temporal Difference Learning for small-board Go | IEEE Conference Publication | IEEE Xplore

Coevolutionary Temporal Difference Learning for small-board Go


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 e...Show More

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.
Date of Conference: 18-23 July 2010
Date Added to IEEE Xplore: 27 September 2010
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Conference Location: Barcelona, Spain

References

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