PP-DTD achieves linear convergence to a neighborhood of the optimum under constant step-sizes and O(T^{-1}) under decaying step-sizes for distributed TD policy evaluation in MARL over directed graphs, claimed as the first with rates comparable to single-agent TD.
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4 Pith papers cite this work. Polarity classification is still indexing.
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A DRL agent learns a direct mapping from channel state information to near-optimal beamforming and hybrid RIS configurations, reaching 95% of the spectral efficiency of alternating optimization at far lower runtime complexity.
DRL learns antenna activation ratio and power coefficients to optimize energy efficiency in cell-free massive MIMO, achieving 50% EE gain and 3350x speedup over sequential convex approximation.
Transformer-based CSI prediction combined with RL algorithms for subspace coordination in MIMO interference alignment yields up to 30% higher average user throughput than baselines in simulations.
citing papers explorer
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Distributed TD Tracking with Linear Function Approximation over Directed Communication Networks
PP-DTD achieves linear convergence to a neighborhood of the optimum under constant step-sizes and O(T^{-1}) under decaying step-sizes for distributed TD policy evaluation in MARL over directed graphs, claimed as the first with rates comparable to single-agent TD.
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Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications
A DRL agent learns a direct mapping from channel state information to near-optimal beamforming and hybrid RIS configurations, reaching 95% of the spectral efficiency of alternating optimization at far lower runtime complexity.
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Deep Reinforcement Learning-Based Dynamic Resource Allocation in Cell-Free Massive MIMO
DRL learns antenna activation ratio and power coefficients to optimize energy efficiency in cell-free massive MIMO, achieving 50% EE gain and 3350x speedup over sequential convex approximation.
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A Novel Reinforcement Learning Based Framework for Scalable MIMO Interference Alignment
Transformer-based CSI prediction combined with RL algorithms for subspace coordination in MIMO interference alignment yields up to 30% higher average user throughput than baselines in simulations.