Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions

TL;DR

This paper introduces V-MCTS, an adaptive variant of Monte-Carlo tree search that allocates more computation to harder states, reducing overall search time while maintaining performance.

Contribution

It presents a novel adaptive search method with theoretical bounds, improving efficiency of MCTS in complex domains.

Findings

Achieves comparable performance to standard MCTS

Reduces search time by over 50% on average

Effective in Go and Atari game environments

Abstract

One of the most important AI research questions is to trade off computation versus performance since ``perfect rationality" exists in theory but is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable attention due to the significant performance improvement in various challenging domains. However, the expensive time cost during search severely restricts its scope for applications. This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that spends more search time on harder states and less search time on simpler states adaptively. We give theoretical bounds of the proposed method and evaluate the performance and computations on $9 \times 9$ Go board games and Atari games. Experiments show that our method can achieve comparable performances to the original search algorithm while requiring less than $50\%$ search time on average. We believe that this approach is a viable alternative for tasks under limited time and resources. The code is available at \url{https://github.com/YeWR/V-MCTS.git}.