The Improvement of Q-learning Applied to Imperfect Information Game

There exist problems of slow convergence and local optimum in standard Q-learning algorithm. Truncated TO estimate returns efficiency and simulated annealing algorithm increase the chance of exploration. To accelerate the algorithm convergence speed and to avoid results in local optimum, this paper combines Q-learning algorithm, truncated TO estimation and simulated annealing algorithm. We apply improved Q-learning algorithm using into the imperfect information game (SiGuo military chess game), and realize a self-learning of imperfect information game system. Experimental outcomes show that this system can dynamically adjust each weight which describes game state according to the results. Further, it speeds up the process of learning, effectively simulates human intelligence and makes reasonable step, and significantly improves system performance.