Evolving Game-Specific UCB Alternatives for General Video Game Playing

  • Ivan Bravi
  • Ahmed Khalifa
  • Christoffer Holmgård
  • Julian Togelius
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

At the core of the most popular version of the Monte Carlo Tree Search (MCTS) algorithm is the UCB1 (Upper Confidence Bound) equation. This equation decides which node to explore next, and therefore shapes the behavior of the search process. If the UCB1 equation is replaced with another equation, the behavior of the MCTS algorithm changes, which might increase its performance on certain problems (and decrease it on others). In this paper, we use genetic programming to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework. Each equation is evolved to maximize playing strength in a single game, but is then also tested on all other games in our test set. For every game included in the experiments, we found a UCB replacement that performs significantly better than standard UCB1. Additionally, evolved UCB replacements also tend to improve performance in some GVGAI games for which they are not evolved, showing that improvements generalize across games to clusters of games with similar game mechanics or algorithmic performance. Such an evolved portfolio of UCB variations could be useful for a hyper-heuristic game-playing agent, allowing it to select the most appropriate heuristics for particular games or problems in general.

Keywords

General AI Genetic programming Monte-Carlo Tree Search 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ivan Bravi
    • 1
  • Ahmed Khalifa
    • 2
  • Christoffer Holmgård
    • 2
  • Julian Togelius
    • 2
  1. 1.Dipartimento di Elettronica, Informatica e BioingegneriaPolitecnico di MilanoMilanoItaly
  2. 2.Tandon School of EngineeringNew York UniversityNew York CityUSA

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