Fast reinforcement learning using asymmetric probability density function

We propose an asymmetric probability density function (PDF) to select an effective action on reinforcement learning (RL). The proposed method utilizing the information of search direction enables RL to reduce the number of trials. Furthermore, the proposed method can be applied easily to various methods of RL, for example, actor-critic, stochastic gradient ascent method. The performance of our proposed method is demonstrated by computer simulations.