… to realistic limitations and sensor errors. In this paper, we propose a deep reinforcement
learning-… to cope effectively with realistic limitations and sensor errors. Additionally, to raise the …

… Meanwhile, in multi-model adaptive estimation in sensor fusion, … Lastly, the architecture used
in deep reinforcement learning, as … the error introduced in the deep reinforcement learning-…

… The system’s state can also be estimated by process sensors or by leveraging soft-sensing
[24]. The reward function in OSEC is parameterized by user interpretable functional …

… deep reinforcement learning algorithms, we in this paper propose a soft actor-critic deep
reinforcement … And similar to our purpose, authors in [11] jointly considered the detection errors …

… been extensively studied in wireless sensor networks (WSNs), … of sensors, etc., the
communication among sensor nodes in a … We use deep reinforcement learning (DRL) to develop …

… deep learning framework built on Torch [22], that has builtin support for deep reinforcement
… in [14], and pseudoHuber loss instead of Mean Squared Error (MSE) as it is more resilient to …

… We error on the optimistic side to run GA for only 50 generations though it is far from
reaching the optimal solution. Even so, SEQ-ALLOC-GA takes much longer time than the RL-based …

… is developing algorithms for active sensing, where the objective is to plan … sensing
resources, such as energy, time, or travel distance. This paper examines the task of active sensing …

… using the estimation error in each sensor setting. … and a sensor placement agent, this study
proposes a stochastic approach to maximize the gain from placing a fixed number of sensors …

… The policy is therefore sufficiently insensitive to input errors in that range of noise levels
to ensure a strong robustness. In addition, it is possible that the decorrelation of these errors …