We study the problem of handling timeliness and criticality trade-off when gathering data from
multiple resources in complex environments. In IoT environments, where several sensors …

… We design a deep reinforcement learning (DRL)-based … that subtly combines deep learning
and reinforcement learning. It is … framework has smaller prediction errors and higher overall …

… Moreover, the transmissions of ground sensors which are far away from the UAVs … errors
at the UAVs. The slow mobility of a UAV can give rise to buffer overflows of the ground sensors …

… deep reinforcement learning in a layered framework and propose a hierarchical deep Q-…
of which is solved using its own reinforcement learning agent. This partitioning simplifies each …

… all the time, the positioning error, the sensing error, the actuator error or even the crosswind,
… It needs some trial and errors with a real UAV. We can characterize as much uncertainty as …

… Comparing with other DSA methods, the proposed DMRL consider multi-user access and
a cooperative DSA network under the presence of spectrum sensing errors. We conducted …

… We propose a multi-agent deep reinforcement learning (… (GALOPP), which incorporates the
limited sensor field-of-view, … , obstacle density, and sensing range on the performance and 2…

… -sensor fusion is used in modern air combat. In this paper, a data fusion method based on
reinforcement learning is developed for multi-sensors… sensors receive the data with large errors…

… -Markov sensor sampling policies that minimize the expected estimation error under the
premise of some energy budget were developed. They enable the system to schedule sensors …

… Deep learning methods, however, require considerable amount of data for training deep …
model for mobile robot navigation using deep reinforcement learning. In our navigation tasks, …