Analysis of Self-Adaptive Monte Carlo Tree Search in General Video Game Playing

Abstract

A purpose of General Video Game Playing (GVGP) is to create agents capable of playing many different real-time video games. Instead of using a fixed general strategy, a challenging aspect is devising strategies that adapt the search to each video game being played. Recent work showed that on-line parameter tuning can be used to adapt Monte-Carlo Tree Search (MCTS) in real-time. This paper extends prior work on Self-adaptive Monte-Carlo Tree Search (SA-MCTS) by further testing one of the previously proposed on-line parameter tuning strategies, based on the N-Tuple Bandit Evolutionary Algorithm (NTBEA). Results show that, both for a simple and a more advanced MCTS agent, on-line parameter tuning has impact on performance only for a few GVGP games. Moreover, an informed strategy as NTBEA shows a significant performance increase only in one case. In a real-time domain as GVGP, advanced parameter tuning does not seem very promising. Randomizing pre-selected parameters for each simulation appears to be a robust approach.