A Multiple-Goal Sarsa(λ) Algorithm Basedon Lost Reward of Greatest Mass
Yuchen Fu · Dianzi xuebao · 2013
For solving the multiple-goal problem in RoboCup,a novel multiple-goal Reinforcement Learning algorithm,named LRGM-Sarsa(λ),is proposed.The algorithm estimates the lost reward of the greatest mass of every sub goal and trades off the long term reward of the sub goals to get a composite policy.In the single learning module,B error function,which is based on MSBR error function is proposed.B error function has guaranteed the convergence of the value prediction with the non-linear function approximation.The probability funciton of selecting actions and the parameter α are also improved with respect to B error function.This algorithm is applied to the training of shooting in Robocup 2D.The experimental results show that the proposed algorithm is more stable and converges faster.