Implementation and Simulation of Cyber Physical System for Robotic Arm Control in Smart Factory

Saif, Saleh; Rahmadani, Firda; Lee, Hyunsoo

doi:10.5391/jkiis.2019.29.4.308

Cited by 2 publications

(1 citation statement)

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“…When the learning object is a robot, these robots are generally classified as robot arms, humanoid robots, and industrial robots [3][4][5]. Robots with a high degree of freedom usually require complicated and difficult calculations because they require a goal that can perfectly mimic human behavior [6].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes

Kim

Lee

2020

Applied Sciences

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In recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more accurately and quickly to maximize the reward. However, the rewards calculated by the system and via human intervention (that make up the learning environment) differ and must be used accordingly. In this paper, we propose a framework for learning the problems of competitive network topologies, wherein the environment dynamically changes agent, by computing the rewards via the system and via human evaluation. The proposed method is adaptively updated with the rewards calculated via human evaluation, making it more stable and reducing the penalty incurred while learning. It also ensures learning accuracy, including rewards generated from complex network topology consisting of multiple agents. The proposed framework contributes to fast training process using multi-agent cooperation. By implementing these methods as software programs, this study performs numerical analysis to demonstrate the effectiveness of the adaptive evaluation framework applied to the competitive network problem depicting the dynamic environmental topology changes proposed herein. As per the numerical experiments, the greater is the human intervention, the better is the learning performance with the proposed framework.

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