… However, most of the reinforcement learning-based navigation gets the path plan … errors.
Therefore, we present a navigation policy for a mobile robot equipped with a 2D range sensor …

… the objective of minimizing the estimation error within each sensing task. The collected data
… Specifically, our deep Q-learning strategy reduced the total estimation error by around 32.4…

… In the following, we investigate the error of spatial inference and the error of temporal infer…
we can use ESL as the only indicator for error minimization. Temporal Deviation: At a certain …

… based approach to minimize the target tracking error and energy consumption in wireless
sensor networks (WSNs). The proposed technique uses a tube-shaped layering method for the …

… in order to minimize the field reconstruction error at the sink. In contrast to the standard ideal
… Our results illustrate that deep reinforcement learning approach can obtain the same error …

… to leverage emerging deep reinforcement learning (DRL) techniques for directing MT’s
sensing and … Note that temporal difference error (TDerror) measures the difference between the …

… we mainly compare the distance error of the lane center and the angle error of the heading
… Since the other opponents’ vehicles are set in the experiment, we add the opponent sensor …

… the error influences the accuracy of the sensor measurement data. For an adequate sensor,
the error is floated intolerance, and most errors … , even though measurement error exists. The …

… operating environment and feed sensing data to a DNN, and then, the DNN processes
the sensing data and renders … Specifically, the maximum error probability is less than 0.5%. …

… reinforcement learning to design optimal sensor placement in this study aided in identifying
10 optimal sensor … the number of sensors did not necessarily reduce errors in measurement. …