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Surprising strategies obtained by stochastic optimization in partially observable games

Abstract : This paper studies the optimization of strategies in the context of possibly randomized two players zero-sum games with incomplete information. We compare 5 algorithms for tuning the parameters of strategies over a benchmark of 12 games. A first evolutionary approach consists in designing a highly randomized opponent (called naive opponent) and optimizing the parametric strategy against it; a second one is optimizing iteratively the strategy, i.e. constructing a sequence of strategies starting from the naive one. 2 versions of coevolutions, real and approximate, are also tested as well as a seed method. The coevolution methods were performing well, but results were not stable from one game to another. In spite of its simplicity, the seed method, which can be seen as an extremal version of coevolution, works even when nothing else works. Incidentally, these methods brought out some unexpected strategies for some games, such as Batawaf or the game of War, which seem, at first view, purely random games without any structured actions possible for the players or Guess Who, where a dichotomy between the characters seems to be the most reasonable strategy. All source codes of games are written in Matlab/Octave and are freely available for download.
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Contributor : Marie-Liesse Cauwet Connect in order to contact the contributor
Submitted on : Wednesday, July 4, 2018 - 11:52:58 AM
Last modification on : Thursday, July 8, 2021 - 3:49:55 AM
Long-term archiving on: : Monday, October 1, 2018 - 2:41:10 PM


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  • HAL Id : hal-01829721, version 1
  • ARXIV : 1807.01877


Marie-Liesse Cauwet, Olivier Teytaud. Surprising strategies obtained by stochastic optimization in partially observable games. CEC 2018 - IEEE Congress on Evolutionary Computation, Jul 2018, Rio de Janeiro, Brazil. pp.1-8. ⟨hal-01829721⟩



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