Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …

Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …

The term “boosting” denotes a powerful means of facilitating machine learning that was
invented by the book's authors 20 years ago and intensively developed since. Despite this …

Potential Games Page 1 GAMES AND ECONOMIC BEHAVIOR 14, 124–143 (1996)
ARTICLE NO. 0044 Potential Games Dov Monderer ∗ Faculty of Industrial Engineering and …

This important text and reference for researchers and students in machine learning, game
theory, statistics and information theory offers a comprehensive treatment of the problem of …

In the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot
machines to play in a sequence of trials so as to maximize his reward. This classical …

Convex programming involves a convex set F⊆ Rn and a convex cost function c: F→ R. The
goal of convex programming is to find a point in F which minimizes c. In online convex …

The standard theory of decision making under uncertainty advises the decision maker to
form a statistical model linking outcomes to decisions and then to choose the optimal …