Parallel Monte Carlo Tree Search in General Video Game Playing

Abstract

Monte Carlo Tree Search (MCTS) parallelization is one of the many possible enhancements for MCTS algorithms. However, no work has been done on evaluating these methods in the rather new area of General Video Game Playing (GVGP), an area that challenges the creation of agents that can play any videogame even without prior knowledge about the video game they are going to play. To address this gap, this paper proposes the implementation and evaluation of the three main MCTS parallelization methods as agents of the General Video Game AI framework, a popular framework for GVGP agents evaluation. This paper is not focused on comparing the parallel MCTS agents to other existing GVGP agents, but rather on exploring how the MCTS parallelization methods compare between themselves. This paper also presents a testing methodology for evaluating these agents, which is based on a set of three experiments focused on different aspects of the parallel MCTS algorithms. In these experiments, the overall best results were achieved by the root parallelization method using the sum merging technique and the UCT’s sigma value of \( \sqrt 2 \).