MCTS with Dynamic Depth Minimax

Hybrid models combining Monte-Carlo Tree Search (MCTS) with fixed depth minimax searches have shown great success as the brute force search allow the model to navigate highly tactical domains. However, minimax is computationally expensive and unnecessary in positions that do not require precise calculations. Ideally, we can adjust the depth to efficiently rely on minimax only when needed. In this paper, we build up the motivation for augmenting MCTS with dynamic depth minimax searches. We analyse the nature of different domains to create some simple dynamic depth adjustment functions which we then benchmark to reinforce our hypothesis that dynamic adjustments of the search depth in MCTS-Minimax hybrids result in stronger play. For this paper we assume that heuristics or evaluator functions are not available to the player, e.g. as in the context of General Game Playing.