Abstract
AbstractIn this study the authors proposed a method for reinforced learning using multistep state estimation based on internal model of control object. It offers a discussion of learning results obtained by application of this model to a vehicle running on a road including various curvatures, for evaluating whether the vehicle would wander off the road. The vehicle's dynamic model acquired by learning was used for estimating rewards and determining the control outputs, based on the estimated states at multiple time intervals. Results of simulation confirmed that safe driving was possible even with road shapes containing nonlearned curvatures and speeds. Since in this control method external information varies depending on the employed control patterns and environmental conditions, it is possible to consider a model with changing view in order to obtain necessary information for driving control. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(10): 85–95, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10123
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