Learning to Play Imperfect-Information Games by Imitating an Oracle Planner

Rinu Boney, Alexander Ilin, Juho Kannala, Jarno Seppanen

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We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have largely focused on model-free learning methods, often requiring hundreds of years of experience to produce competitive agents. Our approach is based on model-based planning. We tackle the problem of partial observability by first building an (oracle) planner that has access to the full state of the environment and then distilling the knowledge of the oracle to a (follower) agent which is trained to play the imperfect-information game by imitating the oracle's choices. We experimentally show that planning with naive Monte Carlo tree search performs poorly in large combinatorial action spaces. We therefore propose planning with a fixed-depth tree search and decoupled Thompson sampling for action selection. We show that the planner is able to discover efficient playing strategies in the games of Clash Royale and Pommerman and the follower policy successfully learns to implement them by training on few hundred battles.

Original languageEnglish
Number of pages11
JournalIEEE Transactions on Games
Publication statusE-pub ahead of print - 2021
MoE publication typeA1 Journal article-refereed


  • Buildings
  • Clash Royale
  • Games
  • Imperfect-information games
  • Law
  • Monte Carlo methods
  • Monte Carlo tree search
  • Observability
  • Planning
  • Poles and towers
  • Pommerman


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