AI RESEARCH
Sim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application
arXiv CS.AI
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ArXi:2603.12020v1 Announce Type: cross Deep Reinforcement Learning (DRL) offers a robust alternative to traditional control methods for autonomous underwater docking, particularly in adapting to unpredictable environmental conditions. However, bridging the "sim-to-real" gap and managing high