Reward Function Learning for Q-learning-based Geographic Routing Protocol

This letter proposes a new scheme that uses Reward Function Learning for Q-learning-based Geographic routing (RFLQGeo) to improve the performance and efficiency of unmanned robotic networks (URNs). High mobility of robotic nodes and changing environments pose challenges for geographic routing protocols; with multiple features simultaneously considered, routing becomes even harder. Q-learning-based...