… Eventually, the sensing error is represented in Fig. 3, where … As depicted, the sensing error
in both cases for the proposed … sensing error and lowest fluctuations in the sensing error by …

… We assume that this (downlink) transmission from gateway to sensor works without errors. …
, resulting in an exponentially reduced error probability. Error performance can be further …

… deep reinforcement learning (DRL) based localization estimators are utilized to estimate
the positions of sensor … In the following, we define H as the error tolerance threshold. If H(ˆxS,j,k+…

… Variable errors, on the other hand, try to emulate the sensing errors that come, for example,
from the vibration of the arm or local odometry errors describing its orientation and position. …

… we propose a deep reinforcement learning-based decision algorithm that uses a deep neural
… 3) The mechanism and sensor errors of the physical robot may cause the intelligent control …

… , reinforcement learning will be used as a sequential decision-making approach that follows
a discrete time stochastic control process. Reinforcement … -and-error process beginning from …

… , we describe deep reinforcement learning and why we are … using sensor data coupled with
deep reinforcement learning to … deep reinforcement learning (deep RL) uses a trial and error …

… of the system via wideband spectrum sensing techniques. The … the presence of spectrum
sensing errors, and the position of … to be sensed will be determined according to the sensing …

… In this letter, we proposed a two-stage reinforcement learning (… However, the reinforcement
learned collision avoidance … -free paths under imperfect sensing, and can well handle noisy …

… (ie, IoT sensors) collects the global channel states as input and uses deep Q-learning to …
data may be imprecise or incomplete due to measurement errors. Specifically, they introduced …