Abstract
Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search heuristic for puzzles and other optimization problems. It achieves state-of-the-art performance on several games including SameGame. In this paper, we design several parallel and distributed NRPA-based search techniques, and we provide a number of experimental insights about their execution. Finally, we use our best implementation to discover 15 better scores for 20 standard SameGame boards.
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CPU: Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40 GHz.
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Acknowledgments
Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several universities as well as other organizations (see https://www.grid5000.fr).
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Negrevergne, B., Cazenave, T. (2018). Distributed Nested Rollout Policy for SameGame. In: Cazenave, T., Winands, M., Saffidine, A. (eds) Computer Games. CGW 2017. Communications in Computer and Information Science, vol 818. Springer, Cham. https://doi.org/10.1007/978-3-319-75931-9_8
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