Revolutionizing O-RAN: Q-Learning Tames Traffic Drift

Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation With Streaming Data

Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especial

Decentralized Multi-Agent Reinforcement Learning for Autonomous Aircraft Traffic Management in AAM Corridor Networks

As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the

AI's Uplink Demand Is Breaking Mobile Networks Built for Downloading

The Ericsson Mobility Report June 2026 documents 3 billion 5G subscriptions and accelerating traffic growth. The finding that matters for en

Understanding Reinforcement Learning for Model Training, and future directions with GRAPE

This paper provides a self-contained, from-scratch, exposition of key algorithms for instruction tuning of models: SFT, Rejection Sampling,

ACM Transactions on Evolutionary Learning and Optimization (telo) Vol 6, Issue 2

Kent Osband's "Rationally Turbulent Expectations" Explores Bayesian Learning with Calm and Turbulent Phases

Kent Osband writes