Research And Implementation Of Computer Game Strategy Based On Reinforcement Learning

As an important branch of artificial intelligence, Computer game has been got extremely rapid development. Computer game is a battle of wits on strategies and research issues. It belongs to problem solving and search technology in artificial intelligence. The core idea of game is actually the combination of evaluation process of the game tree node and game-tree search process. Evaluation is one of the most difficult problems to tackle in game playing. The accuracy of evaluation usually determines the discretion of Game.In this paper, the key technology of game and the relevant principles of Reinforcement Learning were studied. The static evaluation function dependent on human chess knowledge and assessment is inaccurate. Aiming at this problem, BP-TD(λ) algorithm is put forward which combining TD(λ) algorithm with BP neural network. Using BP neural network as the evaluation function of the situation, TD(λ) algorithm can adjust the weights of BP neural network automatically by learning directly from the original experience. The supervised learning of BP neural network is converted into unsupervised learning. The BP neural network is easy to affected by the human experience when adjust parameter values by supervised learning. This algorithm that learning unsupervised can avoid this defect. In order to put training performance into better play, the paper also proposed the strategy of setting parameter values in stages for the opening and the middlegame. When using opening parameter values we choose the method of random selection strategy and When using middlegame parameter values we choose the method of minimax selection strategy.Taking the above-mentioned method and strategy and choosing Renju Game as a model, TDRenju that Renju Game based on reinforcement learning system is implemented. Through the improvements and enhancements of evaluation function, thinking depth is increased.