… by modeling CSI imperfections as Gaussian error [18] to analyze the impact of imperfect
CSI on resource allocation in CR networks. Further, authors in [19] modeled CSI imperfections …

… We consider the scenario in which (i) channels are dynamic and (ii) only partial CSI is …
problem, we propose a multiagent deep reinforcement learning algorithm to solve it, where each …

… Cognitive IoT networks under imperfect CSI. The CSI imperfections are modeled as ellipsoidal
… To solve this problem, Deep Reinforcement Learning based robust scheme is proposed …

… In this context, reinforcement learning-based algorithms that … As exciting as the application
of reinforcement learning to … -continuous space deep reinforcement learning algorithm that …

… imperfect CSI. Due to the FEL training rounds are non-independent, we use a deep reinforcement
learning (… To cope with the dynamic resources and unpredictable CSI in each round, …

… We devise a novel approach, which is combined by deep reinforcement learning (DRL) with
the Lagrangian multiplier to jointly minimize the system cost, where the DRL is employed to …

… Abstract—We introduce a novel deep reinforcement learning (… challenge of imperfect channel
state information (CSI) and … CSI and phase-dependent RIS amplitudes, and mismatched …

… time slot using the CSI of the previous time slot. … imperfect CSI and show our proposed
method outperforms the convex optimization user association scheme in the presence of noisy CSI…

… schemes, reinforcement learning (RL) agent could learn certain … CSI and statistical channel
models, or large numbers of true … and downlink CSI caused by the delay or imperfect channel …

… imperfect CSI, the problem can be formulated as a Partially Observed Markov Decision Process
(POMDP), which can then be solved using deep reinforcement learning (… imperfect CSI. …