Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

Xiali Li, Zhengyu Lv, Licheng Wu, Yue Zhao, Xiaona Xu
2020 Complexity  
In this study, hybrid state-action-reward-state-action (SARSAλ) 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 SARSAλ 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
more » ... or 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.
doi:10.1155/2020/4708075 fatcat:nd4ncim3ybfsnantm4zfaslr3u