{"title":"多评判深度确定性策略梯度无人机路径规划","authors":"Runjia Wu, Fangqing Gu, Jie Huang","doi":"10.1109/CIS52066.2020.00010","DOIUrl":null,"url":null,"abstract":"Deep Deterministic Policy Gradient is a reinforcement learning method, which is widely used in unmanned aerial vehicle (UAV) for path planning. In order to solve the environmental sensitivity in path planning, we present an improved deep deterministic policy gradient for UAV path planning. Simulation results demonstrate that the algorithm improves the convergence speed, convergence effect and stability. The UAV can learn more knowledge from the complex environment.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A multi-critic deep deterministic policy gradient UAV path planning\",\"authors\":\"Runjia Wu, Fangqing Gu, Jie Huang\",\"doi\":\"10.1109/CIS52066.2020.00010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep Deterministic Policy Gradient is a reinforcement learning method, which is widely used in unmanned aerial vehicle (UAV) for path planning. In order to solve the environmental sensitivity in path planning, we present an improved deep deterministic policy gradient for UAV path planning. Simulation results demonstrate that the algorithm improves the convergence speed, convergence effect and stability. The UAV can learn more knowledge from the complex environment.\",\"PeriodicalId\":106959,\"journal\":{\"name\":\"2020 16th International Conference on Computational Intelligence and Security (CIS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 16th International Conference on Computational Intelligence and Security (CIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIS52066.2020.00010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 16th International Conference on Computational Intelligence and Security (CIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS52066.2020.00010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multi-critic deep deterministic policy gradient UAV path planning
Deep Deterministic Policy Gradient is a reinforcement learning method, which is widely used in unmanned aerial vehicle (UAV) for path planning. In order to solve the environmental sensitivity in path planning, we present an improved deep deterministic policy gradient for UAV path planning. Simulation results demonstrate that the algorithm improves the convergence speed, convergence effect and stability. The UAV can learn more knowledge from the complex environment.
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