On Effective Parallelization of Monte Carlo Tree Search

NeurIPS 2020

On Effective Parallelization of Monte Carlo Tree Search

Dec 06, 2020
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Despite its groundbreaking success in Go and computer games, Monte Carlo Tree Search (MCTS) is computationally expensive as it requires a substantial number of rollouts to construct the search tree, which calls for effective parallelization. However, how to design effective parallel MCTS algorithms has not been systematically studied and remains poorly understood. In this paper, we seek to lay its first theoretical foundations, by examining the potential performance loss caused by parallelization when achieving a desired speedup. In particular, we focus on studying the conditions under which the performance loss (measured in excess regret) vanishes over time. To this end, we propose a general parallel MCTS framework that can be specialized to major existing parallel MCTS algorithms. We derive two necessary conditions for the algorithms covered by the general framework to have vanishing excess regret (i.e. excess regret converges to zero as the total number of rollouts grows). We demonstrate the effectiveness of the necessary conditions by showing that, for depth-2 search trees, the recently developed WU-UCT algorithm satisfies both necessary conditions and has provable vanishing excess regret. Finally, we perform empirical studies to closely examine the necessary conditions under the general tree search setting (with arbitrary tree depth). It shows that the topological discrepancy between the search trees constructed by the parallel and the sequential MCTS algorithms is the main reason for the performance loss. Speakers: Anji Liu, Yitao Liang, Ji Liu, Guy Van den Broeck, Jianshu Chen

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