A. Starodubov

Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

Results show that the learned policy achieves tracking performance close to always-on sensing while activating only one sensor per decision time step and avoiding the computationally expensive online entropy search required by expected-information-gain selection.