5 papers
Reinforcement Learning for Long-Horizon Unordered Tasks: From Boolean to Coupled Reward Machines
Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2
Reward machines (RMs) inform reinforcement learning agents about the reward structure of the environment, enabling support for non-Markovian tasks and improving sample efficiency.…
Context-Aware Markov VAE for CSI Compression in Wireless Systems
Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan +2
This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) s…
Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training
Ayush K. Varshney, Konstantinos Vandikas, Šarūnas Girdzijauskas +2
Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss. Quantization-Aware Training (QAT) has emerged as a…
Reinforcement Learning with Reward Machines for Sleep Control in Mobile Networks
Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2
Energy efficiency in mobile networks is crucial for sustainable telecommunications infrastructure, particularly as network densification continues to increase power consumption. Sl…
When to restart? Exploring escalating restarts on convergence
Ayush K. Varshney, Šarūnas Girdzijauskas, Konstantinos Vandikas +1
Learning rate scheduling plays a critical role in the optimization of deep neural networks, directly influencing convergence speed, stability, and generalization. While existing sc…