Anxu Lyu
ABSTRACT
In today's online game, AI (artificial intelligence) has become a widely used technology. Because any game itself is a process of human-computer interaction, it more or less includes the use of AI. In this project, we try to combine the existing AI technology with online game design, and study a feasible online game design road based on AI algorithm. A decision-making model of game AI behavior is designed, and the proposed genetic SASarsa algorithm takes the value of exploration rate as the solution of chromosomes in the genetic process, and also represents the state in the annealing process. In the design of game AI engine, multithreading mechanism and the design pattern of producers and consumers are introduced, and the near-end strategy optimization algorithm is used to improve the search efficiency. Experiments show that reinforcement learning method can use the same reward function for similar intelligent agents, so as to achieve action decisions that conform to the game world view and set after training. It can complete the construction of game AI without reorganizing the decision structure, and improve the intelligence level of intelligent agents in the game.
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