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수중운동체의 롤 제어를 위한 Deep Deterministic Policy Gradient 기반 강화학습
김수용,황연걸,문성웅,Kim, Su Yong,Hwang, Yeon Geol,Moon, Sung Woong 한국군사과학기술학회 2021 한국군사과학기술학회지 Vol.24 No.5
The existing underwater vehicle controller design is applied by linearizing the nonlinear dynamics model to a specific motion section. Since the linear controller has unstable control performance in a transient state, various studies have been conducted to overcome this problem. Recently, there have been studies to improve the control performance in the transient state by using reinforcement learning. Reinforcement learning can be largely divided into value-based reinforcement learning and policy-based reinforcement learning. In this paper, we propose the roll controller of underwater vehicle based on Deep Deterministic Policy Gradient(DDPG) that learns the control policy and can show stable control performance in various situations and environments. The performance of the proposed DDPG based roll controller was verified through simulation and compared with the existing PID and DQN with Normalized Advantage Functions based roll controllers.