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  • 标题:Automating Vehicles by Risk-Averse Preview-based Q-Learning Algorithm
  • 本地全文:下载
  • 作者:Majid Mazouchi ; Subramanya Nageshrao ; Hamidreza Modares
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2022
  • 卷号:55
  • 期号:15
  • 页码:105-110
  • DOI:10.1016/j.ifacol.2022.07.616
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractA risk-averse preview-based Q-learning planner is presented for navigation of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is represented by a finite-state non-stationary Markov decision process (MDP). A sampling-based risk-averse preview-based Q-learning algorithm is finally developed that generates samples using the preview information and reward function to learn risk-averse optimal planning strategies without actual interaction with the environment. The risk factor is imposed on the objective function to avoid fluctuation of the Q values, which can jeopardize the vehicle's safety and/or performance. Theoretical results are provided to bound the number of samples required to guarantee ϵ-optimal planning with a high probability. Finally, to verify the efficiency of the presented algorithm, its implementation on highway driving of an autonomous vehicle in a varying traffic density is considered.
  • 关键词:KeywordsRisk-averseQ-learninglog-expected-exponential Bellman inequality
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