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Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

by , †,‡ and *,†
College of Computer Science and Technology, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Current Address: Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education, Jinlin University, Changchun 130012, China.
Current Address: Chengdu Kestrel Artificial Intelligence Institute, Chengdu 610000, China.
Academic Editors: Renaldas Urniezius and Adam Lipowski
Entropy 2021, 23(6), 737; https://doi.org/10.3390/e23060737
Received: 24 April 2021 / Revised: 1 June 2021 / Accepted: 8 June 2021 / Published: 11 June 2021
An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment. View Full-Text
Keywords: decision-making support system; reinforcement learning; Q-learning decision-making support system; reinforcement learning; Q-learning
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MDPI and ACS Style

Sun, F.; Wang, X.; Zhang, R. Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment. Entropy 2021, 23, 737. https://doi.org/10.3390/e23060737

AMA Style

Sun F, Wang X, Zhang R. Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment. Entropy. 2021; 23(6):737. https://doi.org/10.3390/e23060737

Chicago/Turabian Style

Sun, Fengjie; Wang, Xianchang; Zhang, Rui. 2021. "Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment" Entropy 23, no. 6: 737. https://doi.org/10.3390/e23060737

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