VALUE FUNCTION ESTIMATION BASED ON AN ERROR GAUSSIAN MIXTURE MODEL

In reinforcement, exploration and utilization of agents' action selection has always been the key problem. Agents should not only make full use of maximum action, but also explore potential optimal action. Inspired by the exploration and utilization of actions selection, a novel value function exploration algorithm based on an error Gaussian mixture model (EGMM) is proposed in this paper. First, appropriate variables are chosen from error data, and the number of Gaussian components are obtained by optimizing a Bayesian information criterion via the EGMM. Then, the EGMM is used for the fitting and calculation of error data to obtain the conditional error mean to compensate for the output, thus obtaining more accurate results. We test the performance of the designed algorithm via a virtual experimental platform in a cloud computing environment. Experiments demonstrate the proposed algorithm eliminate the influence of non-Gaussian noise on model prediction performance.