1A1-M14 Reinforcement Learning in Continuous State and Action Spaces : Action Value Functions Expressed by Sigmoid Neural Networks and CMAC(Evolution and Learning for Robotics)

Abstract

This paper proposes a new Q-learning which can construct the mapping function between the continuous state and action space. The proposed method can calculate the expected values of each action by using neural networks and CMAC which is one kind of tiling technics. The proposed method is evaluated through a navigation problem of an autonomous mobile robot.