Deep Reinforcement Learning Using Optimized Monte Carlo Tree Search in EWN

Abstract

EinStein würfelt nicht! (EWN) is a perfect information stochastic game, in which randomness influences the game process enormously. In this article, 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 Upper Confidence Bound Applied to Tree with Heuristic Search, which introduces the expectation valuation strategy into the MCTS. 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%.