This paper examines the problem of real-time optimization of networked systems and
develops online algorithms that steer the system towards the optimal trajectory without …

The paper sets out to obtain precise convergence rates for quasi-stochastic approximation
(QSA), with applications to optimization and reinforcement learning. The main contributions …

Abstract Several decades ago, Profs. Sean Meyn and Lei Guo were postdoctoral fellows at
ANU, where they shared interest in recursive algorithms. It seems fitting to celebrate Lei …

The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic
frameworks for reinforcement learning (RL), and often an efficient approach to solving (or …

Reinforcement learning, as a branch of machine learning, has been gradually applied in the
control field. However, in the practical application of the algorithm, the hyperparametric …

The primary objective of this thesis is to expedite the intricate process of wound healing
through a multifaceted computational approach. Our journey commences by delving into the …