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학술대회 Limit Action Space to Enhance Drone Control with Deep Reinforcement Learning
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장수영, 박노삼
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1212-1215
20ZR1100, 자율적으로 연결·제어·진화하는 초연결 지능화 기술 연구, 박준희
Although many research progresses on deep reinforcement learning, it is not yet perfect. It may take too much time or even fail to solve the problem. Therefore, simplifying the problem by intentionally limiting the agent's action space should help train the agent efficiently and effectively. To verify that, in this paper, we analyze the performances of various action space designs for controlling a drone with deep reinforcement learning. We have designed six different action spaces according to the degree of freedom to analyze the effect of limiting the agent's action space on performance metrics such as travel distance and time, goal rate, and total reward. We show that by limiting the degree of freedom, the agent learns to reach the goal faster with less travel distance and achieve a higher goal rate and reward.
Action Space Design, Deep Reinforcement Learning, Drone Control
KSP 제안 키워드
Action space, Deep reinforcement learning, Degrees of freedom(DOF), Reinforcement Learning(RL), Research progresses, Space design, performance metrics, travel distance