Surrogate-assisted Monte Carlo Tree Search for real-time video games

Man-Je Kim, Donghyeon Lee, Jun Suk Kim, Chang Wook Ahn

Abstract: Highlights•A Monte Carlo tree search (MCTS) method applicable to commercial real-time video games is proposed.•The method integrates MCTS with a deep neural network structure to function efficiently in a real-time environment.•Performance verification of the proposed surrogate-assisted MCTS is done via two commercial real-time video games with distinct features.•Our surrogate model can be trained with a comparatively little amount of computational resources in real-time video games.