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- W4366547891 startingPage "168" @default.
- W4366547891 abstract "Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research." @default.
- W4366547891 created "2023-04-22" @default.
- W4366547891 creator A5001123312 @default.
- W4366547891 creator A5008039520 @default.
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- W4366547891 creator A5024708104 @default.
- W4366547891 creator A5063602029 @default.
- W4366547891 creator A5073958329 @default.
- W4366547891 date "2023-04-20" @default.
- W4366547891 modified "2023-09-30" @default.
- W4366547891 title "A Survey on Reinforcement Learning Methods in Bionic Underwater Robots" @default.
- W4366547891 cites W1999874108 @default.
- W4366547891 cites W2000076730 @default.
- W4366547891 cites W2003754344 @default.
- W4366547891 cites W2014546372 @default.
- W4366547891 cites W2019104368 @default.
- W4366547891 cites W2021776243 @default.
- W4366547891 cites W2031303964 @default.
- W4366547891 cites W2033477502 @default.
- W4366547891 cites W2054770607 @default.
- W4366547891 cites W2117740082 @default.
- W4366547891 cites W2134882417 @default.
- W4366547891 cites W2136719407 @default.
- W4366547891 cites W2145339207 @default.
- W4366547891 cites W2156174987 @default.
- W4366547891 cites W2169498096 @default.
- W4366547891 cites W2169712669 @default.
- W4366547891 cites W2257791260 @default.
- W4366547891 cites W2257979135 @default.
- W4366547891 cites W2417813486 @default.
- W4366547891 cites W2568019971 @default.
- W4366547891 cites W2575705757 @default.
- W4366547891 cites W2605221221 @default.
- W4366547891 cites W2622106568 @default.
- W4366547891 cites W2623317660 @default.
- W4366547891 cites W2746553466 @default.
- W4366547891 cites W2766042994 @default.
- W4366547891 cites W2766447205 @default.
- W4366547891 cites W2793128170 @default.
- W4366547891 cites W2796986051 @default.
- W4366547891 cites W2885871221 @default.
- W4366547891 cites W2911087563 @default.
- W4366547891 cites W2927510627 @default.
- W4366547891 cites W2945720286 @default.
- W4366547891 cites W2964027982 @default.
- W4366547891 cites W2965561449 @default.
- W4366547891 cites W2973788922 @default.
- W4366547891 cites W2980140528 @default.
- W4366547891 cites W2981460880 @default.
- W4366547891 cites W2988458564 @default.
- W4366547891 cites W2990747716 @default.
- W4366547891 cites W2996477139 @default.
- W4366547891 cites W2999008760 @default.
- W4366547891 cites W2999176108 @default.
- W4366547891 cites W3002044607 @default.
- W4366547891 cites W3002469286 @default.
- W4366547891 cites W3004522954 @default.
- W4366547891 cites W3008921485 @default.
- W4366547891 cites W3013722072 @default.
- W4366547891 cites W3017346378 @default.
- W4366547891 cites W3021208093 @default.
- W4366547891 cites W3023256605 @default.
- W4366547891 cites W3038055323 @default.
- W4366547891 cites W3039235589 @default.
- W4366547891 cites W3085141854 @default.
- W4366547891 cites W3096420018 @default.
- W4366547891 cites W3104876774 @default.
- W4366547891 cites W3107524692 @default.
- W4366547891 cites W3109091134 @default.
- W4366547891 cites W3109589982 @default.
- W4366547891 cites W3116922584 @default.
- W4366547891 cites W3126763268 @default.
- W4366547891 cites W3133183884 @default.
- W4366547891 cites W3138984732 @default.
- W4366547891 cites W3153187775 @default.
- W4366547891 cites W3156506101 @default.
- W4366547891 cites W3158253560 @default.
- W4366547891 cites W3160469717 @default.
- W4366547891 cites W3167148272 @default.
- W4366547891 cites W3177450727 @default.
- W4366547891 cites W3184180881 @default.
- W4366547891 cites W3185343670 @default.
- W4366547891 cites W3187378924 @default.
- W4366547891 cites W3197878257 @default.
- W4366547891 cites W3198050371 @default.
- W4366547891 cites W3201993369 @default.
- W4366547891 cites W3205608407 @default.
- W4366547891 cites W3206624953 @default.
- W4366547891 cites W3207723366 @default.
- W4366547891 cites W3212670435 @default.
- W4366547891 cites W3215346265 @default.
- W4366547891 cites W32403112 @default.
- W4366547891 cites W4200365144 @default.
- W4366547891 cites W4214873511 @default.
- W4366547891 cites W4221132833 @default.
- W4366547891 cites W4224299039 @default.
- W4366547891 cites W4224306307 @default.
- W4366547891 cites W4234301890 @default.