The invention provides a neural network and Q learning combined estimation method under non-complete information, and the method comprises the steps: 1, converting the non-complete information into a partly observable Markov decision-making model; 2, converting the non-complete information gaming into complete information gaming through the Monte Carlo sampling technology; 3, calculating Q learning delay return value through a Q learning algorithm based on former n steps, an algorithm combining a neural network and the Q learning and an algorithm UCT based on an upper limit confidence interval; 4, carrying out the fusion of the Q value obtained at a former step, and obtaining a final result. According to the technical scheme of the invention, the method can be used in various types of non-complete information gaming, such as the Chinese poker and Texas Hold'em poker, and improves the gaming level of an intelligent agent. Compared with the conventional related research, the method greatly improves the precision.
Neural network and Q learning combined estimation method under non-complete information