A Dive into Biologically-Inspired Experience Replay

In the realm of advanced robotics, particularly in unmanned underwater vehicles (UUVs), the ability to adaptively respond to environmental changes is crucial. Typically, UUVs rely on feedback from velocity and orientation sensors to navigate and maintain stability against natural disturbances like ocean currents. However, traditional methods often fall short, particularly in complex dynamic environments, as they cannot fully compensate for high-frequency disturbances. This study introduces a novel approach, leveraging a biologically-inspired mechanism within deep reinforcement learning to enhance the UUV's adaptiveness.