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Document type:
Konferenzbeitrag 
Contribution type:
Textbeitrag / Aufsatz 
Author(s):
Bernhard, Julian; Gieselmann, Robert ; Esterle, Klemens; Knoll, Alois 
Title:
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning 
Abstract:
Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search- or optimization-based motion planners. With Deep Reinforcement Learning, optimal driving strategies for such problems can be derived also for higher- dimensional problems. However, these methods guarantee optimality of the resulting policy only in a statistical sense, which impedes their usage in safety critical systems, such as autonomous vehicl...    »
 
Keywords:
learning (artificial intelligence); mobile robots; optimal control; path planning; robust control; search problems; statistical analysis; deep Q-network; experience-based-heuristic-search algorithm; deep-reinforcement-learning-based planner; search-based motion planners; self-driving vehicles; semistructured valet parking scenarios; path planning; pre-learned optimal policy; statistical failure rate; autonomous vehicles; safety critical systems; higher-dimensional problems; optimal driving strat...    »
 
Editor:
IEEE 
Book / Congress title:
2018 21st International Conference on Intelligent Transportation Systems (ITSC) 
Year:
2018 
Pages:
3175-3182 
Reviewed:
ja