Deep Reinforcement Learning Using Optimized Monte Carlo Tree Search in EWN
Yixian Zhang, Zhuoxuan Li, Yiding Cao, Xuan Zhao, Jinde Cao · IEEE Transactions on Games · 2023
EinStein würfelt nicht! is a perfect information stochastic game in which randomness influences the game process enormously. In this paper, we propose an optimized algorithm named Quick Neural Network Tree Search (QNNTS) based on deep reinforcement learning and Monte Carlo tree search (MCTS) to construct the artificial intelligence agent of EWN. Meanwhile the lightness of the model makes it possible to train with much less computing resources. The optimization structure of the algorithm based on MCTS is named Optimized UCT with Heuristic Search (UCTH), which introduces the expectation valuation strategy into the Monte Carlo tree search. As the prerequisite product of QNNTS, it performs with an improvement of the winning rate. Ultimately, the Attention-ResNet structure combined with domain knowledge is used to obtain the proposed algorithm. Compared with several conventional algorithms, it gains high winning rates of at least 68%.