HIGHLIGHTS
- What: The authors examine the future directions of integrating and intellectualizing these key technologies, providing both theoretical support and practical guidance to advance technological progress and promote the practical application of this field. This study shows that deep reinforcement learning can effectively control physical systems and manage com16 of10 26 plex control tasks in real-world applications without relying on a complete model. In response to the complexities of underwater environments, research in net-cleaning robotics is focusing on key technological breakthroughs to advance automation technologies.
- Who: Heng Liu et al. from the College of . . .
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