Self-Adaptive Monte Carlo Tree Search in General Game Playing

Abstract

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

Self-Adaptive Rolling Horizon Evolutionary Algorithms for General Video Game Playing

“…Similarly, an on-line mechanism was used to find the best parameter configuration for an MCTS agent depending on the game being played [6], [7]. In this approach, the result of each MCTS simulation is used to evaluate the quality of the parameter values that control the simulation.…”

“…Moreover, statistics collected so far on the performance of parameter values are used to choose which values to evaluate next. This approach has been tested both on classic board games [6] and on arcade-style video games [7]. On board games, it had positive results, especially when the number of tuned parameters is small.…”

“…Therefore, the problem of tuning these parameters for each new game consists in searching the optimal vector of parameters values in a combinatorial search space. Previous work [7] defined this problem as a Combinatorial Multi-Armed Bandit, characterised by the following three components:…”

“…For simulation-based agents, like MCTS, previous research proposed a possible way to address this problem, which consists in a method that tunes search-control parameters on-line, i.e. while playing a single run of a game [6], [7]. For evolutionary-based GVGP agents like the ones based on RHEA, however, no attempt at on-line adaptation has been made so far.…”

“…To do so, the on-line tuning method previously proposed for MCTS is adapted. Different allocation strategies have also been proposed in previous work for on-line parameter tuning of MCTS agents [6], [7]. These strategies decide how to distribute the available samples to evaluate the various parameter value combinations.…”