TY - GEN
T1 - Control of Quadrotor Drone with Partial State Observation via Reinforcement Learning
AU - Shan, Guangcun
AU - Zhang, Yinan
AU - Gao, Yong
AU - Wang, Tian
AU - Chen, Jianping
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - In this paper, we propose a quadrotor control algorithm which use the historical strengthened partial state observations as input information to control the quadrotor drone using reinforcement learning algorithm. Reinforcement learning method could enable the agent to learn a policy which could map the observations to control commands, which, in our work, is the actuator command of the quadrotor. Besides, we conduct our method in the control task via simulation, the results of which show excellent performance.
AB - In this paper, we propose a quadrotor control algorithm which use the historical strengthened partial state observations as input information to control the quadrotor drone using reinforcement learning algorithm. Reinforcement learning method could enable the agent to learn a policy which could map the observations to control commands, which, in our work, is the actuator command of the quadrotor. Besides, we conduct our method in the control task via simulation, the results of which show excellent performance.
KW - partial state observation
KW - quadrotor control
KW - quadrotor drone
KW - reinforcement learning
UR - https://www.scopus.com/pages/publications/85080048303
U2 - 10.1109/CAC48633.2019.8996394
DO - 10.1109/CAC48633.2019.8996394
M3 - 会议稿件
AN - SCOPUS:85080048303
T3 - Proceedings - 2019 Chinese Automation Congress, CAC 2019
SP - 1965
EP - 1968
BT - Proceedings - 2019 Chinese Automation Congress, CAC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Chinese Automation Congress, CAC 2019
Y2 - 22 November 2019 through 24 November 2019
ER -