In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

In the last few years, reinforcement learning (RL), also called adaptive (or approximate)
dynamic programming, has emerged as a powerful tool for solving complex sequential …

The original work in recursive stochastic algorithms was by Robbins and Monro, who
developed and analyzed a recursive procedure for finding the root of a real-valued function …

We consider the fully decentralized multi-agent reinforcement learning (MARL) problem,
where the agents are connected via a time-varying and possibly sparse communication …

Stochastic approximation was introduced in a 1951 article in the Annals of Mathematical
Statistics by Robbins and Monro. Originally conceived as a tool for statistical computation …

We present four new reinforcement learning algorithms based on actor-critic and natural-
gradient ideas, and provide their convergence proofs. Actor-critic rein-forcement learning …

This book is written for students and researchers in the field of industrial engineering,
computer science, operations research, management science, electrical engineering, and …

It is shown here that stability of the stochastic approximation algorithm is implied by the
asymptotic stability of the origin for an associated ODE. This in turn implies convergence of …

The effective operation of time-critical Internet of things (IoT) applications requires real-time
reporting of fresh status information of underlying physical processes. In this paper, a real …

The field of multi-agent system (MAS) is an active area of research within artificial
intelligence, with an increasingly important impact in industrial and other real-world …