Two-level transposition table optimization algorithm based on deep reinforcement learning
Computer game programs based on deep reinforcement learning, such as AlphaGo, have beaten human world champions in the game of Go.These algorithms utilize learnable value neural networks and policy neural networks to guide the exploration process of Monte Carlo Tree Search.Various enhancement methods have been proposed to improve the search performance of Monte Carlo trees, among which the transposition table has been proven to enhance search efficiency.Building upon this foundation, this paper introduces a novel method, the two-level transposition table optimization algorithm based on deep reinforcement learning.This method manages two level transposition tables using distinct replacement strategies and decouples the two-step moves of Connect6 into two independent neural networks.This not only reduces the scale of the action space but also simplifies neural network training.Our experimental results using Connect6 as an example demonstrate this approach significantly enhances the program ' s playing strength under limited computational resources.
deep reinforcement learningtransposition tablecomputer gameAlphaGoMCTS
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