Improvement of Particle Filter for Reinforcement Learning

In this paper, we propose a novel framework of learning that uses a particle filter. In a real-world situation, it is difficult to express a continuous state and a continuous action. The problem is solved by using our particle filter, which is one of the methods for dividing a continuous state and a continuous action. Our method needs only a small number of memories and parameters for searching the solution in the space. We conducted pendulum and double-pendulum simulations and observed the difference between the conventional method and the proposed method. Simulation results show there was no bad effect on the received reward.