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Couple Particles in Action Space for Reinforcement Learning


Akira Notsu, Katsuhiro Honda, Hidetomo Ichihashi


Vol. 10  No. 12  pp. 200-203


In this paper, we propose a novel action-search particle-filtering algorithm for learning processes. This algorithm is designed to perform search domain reduction and heuristic space segmentation. In this method, each action space is divided into new two segments using two 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.


Reinforcement Learning, TD-Learning, Particle Filter, Single Pendulum Simulation