CMAC-based Sarsa (λ) Learning Algorithm for RoboCup-soccer Goalkeeper

RoboCup simulated soccer has a large and complex state space,at the same time the variables used for decision are usually continuous,that make it difficult for the agent to choose the optimal action.This paper presents the goalkeeper as a case study,based on CMAC neural network,the continuous state space is firstly generalized,and then the Sarsa (λ) learning algorithm is employed to find the optimal policy.The author empirically evaluated and compared the defending effect of the goalkeepers with different strategies.Simulation results show that the goalkeeper with the learning algorithm has better defending effect and its defending time increases obviously after a period of time.