Monte Carlo Tree Search with options for general video game playing

General video game playing is a challenging research area in which the goal is to find one algorithm that can play many games successfully. 'Monte Carlo Tree Search' (MCTS) is a popular algorithm that has often been used for this purpose. It incrementally builds a search tree based on observed states after applying actions. However, the MCTS algorithm always plans over actions and does not incorporate any higher level planning, as one would expect from a human player. Furthermore, although many games have similar game dynamics, often no prior knowledge is available to general video game playing algorithms. In this paper, we introduce a new algorithm called 'Option Monte Carlo Tree Search' (O-MCTS). It offers general video game knowledge and high level planning in the form of 'options', which are action sequences aimed at achieving a specific subgoal. Additionally, we introduce 'Option Learning MCTS' (OL-MCTS), which applies a progressive widening technique to the expected returns of options in order to focus exploration on fruitful parts of the search tree. Our new algorithms are compared to MCTS on a diverse set of twenty-eight games from the general video game AI competition. Our results indicate that by using MCTS's efficient tree searching technique on options, O-MCTS outperforms MCTS on most of the games, especially those in which a certain subgoal has to be reached before the game can be won. Lastly, we show that OL-MCTS improves its performance on specific games by learning expected values for options and moving a bias to higher valued options.