An adaptive actor-critic algorithm with multi-step simulated experiences for controlling nonholonomic mobile robots

In this paper, we propose a new algorithm of an adaptive actor-critic method with multi-step simulated experiences, as a kind of temporal difference (TD) method. In our approach, the TD-error is composed of two value- functions and m utility functions, where m denotes the number of multi-steps in which the experience should be simulated. The value-function is constructed from the critic formulated by a radial basis function neural network (RBFNN), which has a simulated experience as an input, generated from a predictive model based on a kinematic model. Thus, since our approach assumes that the model is available to simulate the m-step experiences and to design a controller, such a kinematic model is also applied to construct the actor and the resultant model based actor (MBA) is also regarded as a network, i.e., it is just viewed as a resolved velocity control network. We implement this approach to control nonholonomic mobile robot, especially in a trajectory tracking control problem for the position coordinates and azimuth. Some simulations show the effectiveness of the proposed method for controlling a mobile robot with two-independent driving wheels.