10 citations · 19 across the 6 of their papers we have counts for
6 papers
Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networks
Talha Bozkus, Urbashi Mitra
Q-learning is a widely used reinforcement learning (RL) algorithm for optimizing wireless networks, but faces challenges with large state-spaces. Recently proposed multi-environmen…
Coverage Analysis for Digital Cousin Selection -- Improving Multi-Environment Q-Learning
Talha Bozkus, Tara Javidi, Urbashi Mitra
Q-learning is widely employed for optimizing various large-dimensional networks with unknown system dynamics. Recent advancements include multi-environment mixed Q-learning (MEMQ)…
A Multi-Agent Multi-Environment Mixed Q-Learning for Partially Decentralized Wireless Network Optimization
Talha Bozkus, Urbashi Mitra
Q-learning is a powerful tool for network control and policy optimization in wireless networks, but it struggles with large state spaces. Recent advancements, like multi-environmen…
Coverage Analysis of Multi-Environment Q-Learning Algorithms for Wireless Network Optimization
Talha Bozkus, Urbashi Mitra
Q-learning is widely used to optimize wireless networks with unknown system dynamics. Recent advancements include ensemble multi-environment hybrid Q-learning algorithms, which uti…
Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks
Talha Bozkus, Urbashi Mitra
Optimizing large-scale wireless networks, including optimal resource management, power allocation, and throughput maximization, is inherently challenging due to their non-observabl…
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy Optimization
Talha Bozkus, Urbashi Mitra
Reinforcement learning (RL) is a classical tool to solve network control or policy optimization problems in unknown environments. The original Q-learning suffers from performance a…