Autonomous   Vehicle Navigation Behavior Generation by Reinforcement Learning

Keitaro Naruse; Yukinori Kakazu; Ming C. Leu

doi:10.20965/jrm.1998.p0413

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JRM Vol.10 No.5 pp. 413-417

doi: 10.20965/jrm.1998.p0413

(1998)

Paper:

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Autonomous Vehicle Navigation Behavior Generation by Reinforcement Learning

Keitaro Naruse^* and Yukinori Kakazu^* and Ming C. Leu^**

^*Hokkaido University, Kita-13 Nishi-8, Sapporo 060 Japan

^**New Jersey Institute of Technology, Newark, NJ 07102, USA

Received:

April 3, 1998

Accepted:

August 17, 1998

Published:

October 20, 1998

Keywords:

Reinforcement learning, Behavior robotics, Autonomous vehicle navigation, Path planning, Nonholononic vehicle

Abstract

This paper presents an efficient reinforcement learning algorithm for autonomous vehicle navigation. Efficiency is achieved by identifying the structure of a given problem, and it is represented as a set of behaviors – efficient action sequences for solving the problem. Computational simulations are conducted and the proposed mechanism demonstrate.

Cite this article as:

Keitaro Naruse, Yukinori Kakazu, and Ming C. Leu, “Autonomous Vehicle Navigation Behavior Generation by Reinforcement Learning,” J. Robot. Mechatron., Vol.10, No.5, pp. 413-417, 1998.

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Autonomous Vehicle Navigation Behavior Generation by Reinforcement Learning

Keitaro Naruse* and Yukinori Kakazu* and Ming C. Leu**

Keitaro Naruse^* and Yukinori Kakazu^* and Ming C. Leu^**