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Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter
George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos
Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequ…
Filtering Jump Markov Systems with Partially Known Dynamics: A Model-Based Deep Learning Approach
George Stamatelis, George C. Alexandropoulos
This paper presents the Jump Markov Filtering Network (JMFNet), a novel model-based deep learning framework for real-time state-state estimation in jump Markov systems with unknown…
Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems
George Stamatelis, Angelos-Nikolaos Kanatas, George C. Alexandropoulos
Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a powerful tool for optimizing decentralized decision-making systems in complex settings, such as Dynamic Spectrum Ac…