Do Yeong Jeon, Myeong Ho Song, Soo Dong Kim, Journal of Internet Computing and Services, Vol. 22, No. 4, pp. 13-28, Aug. 2021
10.7472/jksii.2021.22.4.13, Full Text:  HTML
Keywords: Reinforcement Learning (RL), Advanced RL, Formal Model, Design Methods, and Advanced Navigator System

Abstract

Reinforcement Learning (RL) is a machine learning algorithm that repeat the closed-loop process that agents perform actions specified by the policy, the action is evaluated with a reward function, and the policy gets updated accordingly. The key benefit of RL is the ability to optimze the policy with action evaluation. Hence, it can effectively be applied to developing advanced intelligent systems and autonomous systems. Conventional RL incoporates a single policy, a reward function, and relatively simple policy update, and hence its utilization was limited. In this paper, we propose an extended RL model that considers multiple instances of RL elements. We define a formal model of the key elements and their computing model of the extended RL. Then, we propose design methods for applying to system development. As a case stud of applying the proposed formal model and the design methods, we present the design and implementation of an advanced car navigator system that guides multiple cars to reaching their destinations efficiently.

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