Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search

Monte Carlo Tree Search (MCTS) has proven to be a staple method in Game Artificial Intelligence for creating agents that can perform well in complex environments without requiring domain-specific knowledge. The main downside of this planning based algorithm is the high computational budget needed to recommend an action. The fundamental cause of this is a vast search space caused by a high branching factor, and the difficulty to create a good heuristic function to guide the search without leveraging domain-specific knowledge. Recent advances in the field proposed a new planning based method called Monte Carlo Graph Search (MCGS), which uses a graph instead of a tree to plan its next action, reducing the branching factor and consequently increasing the performance of the search. In this paper, we propose several modifications that optimize the performance by increasing the sample efficiency of MCGS. The use of frontier for node selection, improving the rollout phase by doing stored rollouts, and a generalized approach to guide the search by incorporating a domain-independent online novelty detection method. Together these enhancements enable MCGS to solve sparse reward environments while using a significantly lower computational budget than MCTS.