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

Multi-agent reinforcement learning (MARL) refers to systems in which multiple agents are
acting in a common and unknown environment. The presence of other agents makes MARL …

We propose a theoretical framework for approximate planning and learning in partially
observed systems. Our framework is based on the fundamental notion of information state …

A mean-field-type game is a game in which the instantaneous payoffs and/or the state
dynamics functions involve not only the state and the action profile but also the joint …

In this article, we consider sequential dynamic team decision problems with nonclassical
information structures. First, we address the problem from the point of view of a “manager” …

A decentralized control system with linear dynamics, quadratic cost, and Gaussian
disturbances is considered. The system consists of a finite number of subsystems whose …

This work studies the behaviors of two large-population teams competing in a discrete
environment. The team-level interactions are modeled as a zero-sum game while the agent …

In their seminal work, Nayyar et al.(2013) showed that imperfect information can be
abstracted away from common-payoff games by having players publicly announce their …

For artificially intelligent learning systems to have widespread applicability in real-world
settings, it is important that they be able to operate decentrally. Unfortunately, decentralized …

Inspired by the concepts of deep learning in artificial intelligence and fairness in behavioral
economics, we introduce deep teams in this article. In such systems, agents are partitioned …