We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess.
- Authors
Li, Xiali; Lv, Zhengyu; Wu, Licheng; Zhao, Yue; Xu, Xiaona
- Abstract
In this study, hybrid state-action-reward-state-action (SARSA λ ) 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 λ 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.
- Subjects
REINFORCEMENT learning; CHESS; DEEP learning; LEARNING strategies
- Publication
Complexity, 2020, p1
- ISSN
1076-2787
- Publication type
Article
- DOI
10.1155/2020/4708075