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Combining Myopic Optimization and Tree Search: Application to MineSweeper

Michèle Sebag 1, 2 Olivier Teytaud 1, 2 
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Many reactive planning tasks are tackled by optimization combined with shrinking horizon at each time step: the problem is sim- plified to a non-reactive (myopic) optimization problem, based on the available information at the current time step and an estimate of future behavior, then it is solved; and the simplified problem is updated at each time step thanks to new information. This is in particular suitable when fast off-the-shelf components are available for the simplified problem - optimality stricto sensu is not possible, but good results are obtained at a reasonnable computational cost for highly untractable problems. As machines get more powerful, it makes sense however to go beyond the inherent limitations of this approach. Yet, a brute-force solving of the complete problem is often impossible; we here propose a methodology for embedding a solver inside a consistent reactive planning solver. Our methodology consists in embedding the solver in an Upper- Confidence-Tree algorithm, both in the nodes and as a Monte-Carlo simulator. We show the mathematical consistency of the approach, and then we apply it to a classical success of the myopic approach: the MineSweeper game.
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Submitted on : Tuesday, February 19, 2013 - 4:34:48 PM
Last modification on : Sunday, June 26, 2022 - 11:58:19 AM
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  • HAL Id : hal-00712417, version 2



Michèle Sebag, Olivier Teytaud. Combining Myopic Optimization and Tree Search: Application to MineSweeper. LION6, Learning and Intelligent Optimization, Proc. LION 6, 2012, Paris, France. pp.222-236. ⟨hal-00712417v2⟩



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