반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

시뮬레이션 환경에서의 DQN을 이용한 강화 학습 기반의 무인항공기 경로 계획

Path Planning of Unmanned Aerial Vehicle based Reinforcement Learning using Deep Q Network under Simulated Environment

  • 이근형 (연세대학교 컴퓨터과학과) ;
  • 김신덕 (연세대학교 컴퓨터과학과)
  • 투고 : 2017.09.21
  • 심사 : 2017.09.23
  • 발행 : 2017.09.30
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초록

In this research, we present a path planning method for an autonomous flight of unmanned aerial vehicles (UAVs) through reinforcement learning under simulated environment. We design the simulator for reinforcement learning of uav. Also we implement interface for compatibility of Deep Q-Network(DQN) and simulator. In this paper, we perform reinforcement learning through the simulator and DQN, and use Q-learning algorithm, which is a kind of reinforcement learning algorithms. Through experimentation, we verify performance of DQN-simulator. Finally, we evaluated the learning results and suggest path planning strategy using reinforcement learning.

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참고문헌

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