Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Formation planning for multiple USVs with PFCL-MADDPG deep reinforcement learning: theory and experiment. |
| Συγγραφείς: |
Zhou, Jiahao1 (AUTHOR), Wang, Hao1,2 (AUTHOR) wanghao@mail.neu.edu.cn, Fu, Jun1 (AUTHOR), Xu, Hongli1 (AUTHOR) |
| Πηγή: |
International Journal of Systems Science. Oct2026, Vol. 57 Issue 14, p4641-4655. 15p. |
| Θεματικοί όροι: |
*Risk assessment, Robotic path planning, Reinforcement learning, Scientific experimentation, Autonomous robots, Machine learning, Maritime management |
| Περίληψη: |
In this paper, we propose a novel algorithm called Potential Field and Curriculum Learning-based Multi-Agent Deep Deterministic Policy Gradient (PFCL-MADDPG), which is based on curriculum learning and collision risk assessment model. This algorithm excels in handling complex path planning and sparse reward scenarios. By incorporating curriculum learning, we mitigate the challenges posed by sparse rewards, thereby enhancing learning efficiency. Additionally, we introduce Artificial Potential Field (APF) to establish a collision risk assessment model that computes reward values, significantly improving the safety of USVs. We conducted experiments with multiple USVs in both simulated and real-world environments. The results demonstrate that our algorithm exhibits remarkable robustness and effectiveness when executing tasks in complex maritime environments. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
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