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arXiv · 1903.01747

Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning

Ziyu Liu·Meng Zhou·Weiqing Cao·Qiang Qu·Henry Wing Fung Yeung·Vera Yuk Ying Chung
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Abstract

The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second, unlike Go, there are also no upper bounds on the depth of the search tree since repetitions and backward movements are allowed. Therefore, even in a restricted game instance, the state-space of the game can still be unbounded, making it challenging for a computer program to excel. In this work, we present an approach that effectively combines the use of heuristics, Monte Carlo tree search, and deep reinforcement learning for building a Chinese Checkers agent without the use of any human game-play data. Experiment results show that our agent is competent under different scenarios and reaches the level of experienced human players.

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Ziyu Liu, Meng Zhou, Weiqing Cao, Qiang Qu, Henry Wing Fung Yeung, Vera Yuk Ying Chung. 2019-03-05. Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning. https://arxiv.org/abs/1903.01747

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