In the real world, agents or entities are in a continuous state of interactions. These
interactions lead to various types of complexity dynamics. One key difficulty in the study of …

We investigate safe multi-agent reinforcement learning, where agents seek to collectively
maximize an aggregate sum of local objectives while satisfying their own safety constraints …

This chapter describes a number of results obtained in the last sixty years in the theory of
non-zero-sum discrete-time stochastic games. We overview almost all basic streams of …

The purpose of this paper is to understand phase transition in noncooperative dynamic
games with a large number of agents. Applications are found in neuroscience, biology, and …

In this paper, the problem of the smart grid energy management under stochastic dynamics
is investigated. In the considered model, at the demand side, it is assumed that customers …

Constrained Markov game is a fundamental problem that covers many applications, where
multiple players compete with each other under behavioral constraints. The existing …

We study in this paper optimal stochastic control issues in delay tolerant networks. We first
derive the structure of optimal 2-hop forwarding policies. In order to be implemented, such …

We consider the problem of budget (or other resource) allocation in sequential decision
problems involving a large number of concurrently running sub-processes, whose only …

We examine online safe multi-agent reinforcement learning using constrained Markov
games in which agents compete by maximizing their expected total rewards under a …

In task offloading systems, it is imperative to guarantee that an offloaded task is completed
within a pre-specified deadline. In this paper, we propose a distributed device-to-device …