Proposed particle-filtering method for reinforcement learning

We propose a novel action-search particle-filtering algorithm for reinforcement learning processes. This algorithm is designed to perform search domain reduction and heuristic space segmentation. In this method, each action space is divided into several new segments using particles. Appropriate search domain reduction can minimize learning time and enable the recognition of the evolutionary process of learning. In a numerical experiment, the proposed filtering method is applied to a single-pendulum simulation in order to demonstrate the adaptability of this simulation model.