제어로봇시스템학회:학술대회논문집
- 2001.10a
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- Pages.43.6-43
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- 2001
Adaptive Actor-Critic Learning of Mobile Robots Using Actual and Simulated Experiences
- Rafiuddin Syam (Saga Univ.) ;
- Keigo Watanabe (Saga Univ.) ;
- Kiyotaka Izumi (Saga Univ.) ;
- Kazuo Kiguchi (Saga Univ.) ;
- Jin, Sang-Ho (Doowon Technical College)
- Published : 2001.10.01
Abstract
In this paper, we describe an actor-critic method as a kind of temporal difference (TD) algorithms. The value function is regarded as a current estimator, in which two value functions have different inputs: one is an actual experience; the other is a simulated experience obtained through a predictive model. Thus, the parameter´s updating for the actor and critic parts is based on actual and simulated experiences, where the critic is constructed by a radial-basis function neural network (RBFNN) and the actor is composed of a kinematic-based controller. As an example application of the present method, a tracking control problem for the position coordinates and azimuth of a nonholonomic mobile robot is considered. The effectiveness is illustrated by a simulation.
Keywords