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Nested Rollout Policy Adaptation with Selective Policies

  • Tristan CazenaveEmail author
Conference paper
  • 511 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 705)

Abstract

Monte Carlo Tree Search (MCTS) is a general search algorithm that has improved the state of the art for multiple games and optimization problems. Nested Rollout Policy Adaptation (NRPA) is an MCTS variant that has found record-breaking solutions for puzzles and optimization problems. It learns a playout policy online that dynamically adapts the playouts to the problem at hand. We propose to enhance NRPA using more selectivity in the playouts. The idea is applied to three different problems: Bus regulation, SameGame and Weak Schur numbers. We improve on standard NRPA for all three problems.

Keywords

Selective Policy Monte Carlo Tree Search (MCTS) Playout Policy MCTS Variant Nested Monte-Carlo Search (NMCS) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.PSL-Université Paris-Dauphine, LAMSADE CNRS UMR 7243ParisFrance

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