Informed Hybrid Game Tree Search for General Video Game Playing

In this paper, we introduce a universal game playing agent that is able to successfully play a wide variety of video games. It combines the strengths of Monte Carlo tree search with conventional heuristic search into a single hybrid search agent, which is able to select the appropriate strategy based on its observations about the game dynamics. In particular, the agent learns a knowledge base which provides the agent with information such as an approximate transition function, the type of agents and objects that participate in the game and the possible effects of interacting with them, heuristics for focusing and pruning the search, and more. This hybrid strategy proved to be successful in the 2015 General Video Game Competition, in which our agent emerged as the clear winner.