Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

Xiali Li, Zhengyu Lv, Licheng Wu, Yue Zhao and Xiaona Xu

Complexity, 2020, vol. 2020, 1-11

Abstract:

In this study, hybrid state-action-reward-state-action (SARSA ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSA and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:4708075

DOI: 10.1155/2020/4708075

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