Reinforcement Learning information for Autonomous Marine Data Collection

Autonomous data collection using Unmanned Surface Vehicles is crucial for applications such as marine ranching but remains challenging in uncertain ocean environments. This study proposes the Risk-Adaptive Implicit Quantile Network, a brain-inspired framework integrating distributional reinforcement learning with dynamic risk regulation.