10 citations · 22 across the 7 of their papers we have counts for
6 papers · 1 filter
From Relative Entropy to Minimax: A Unified Framework for Coverage in MDPs
Xihe Gu, Urbashi Mitra, Tara Javidi
Targeted and deliberate exploration of state--action pairs is essential in reward-free Markov Decision Problems (MDPs). More precisely, different state-action pairs exhibit differe…
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)…
Asymmetric Graph Error Control with Low Complexity in Causal Bandits
Chen Peng, Di Zhang, Urbashi Mitra
In this paper, the causal bandit problem is investigated, with the objective of maximizing the long-term reward by selecting an optimal sequence of interventions on nodes in an unk…
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…