We show that computing approximate stationary Markov coarse correlated equilibria (CCE)
in general-sum stochastic games is PPAD-hard, even when there are two players, the game …

Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential
sample complexity dependence on the number of agents, a phenomenon known as the …

A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the\emph {curse of
multiagency}, where the description length of the game as well as the complexity of many …

Game theory studies the mathematical models for self-interested individuals. Nash
equilibrium is arguably the most central solution in game theory. While finding the Nash …

Mean-field games have been used as a theoretical tool to obtain an approximate Nash
equilibrium for symmetric and anonymous $ N $-player games. However, limiting …

We study a new class of Markov games,\textit {(multi-player) zero-sum Markov Games} with
{\it Networked separable interactions}(zero-sum NMGs), to model the local interaction …

We propose a new model,\emph {independent linear Markov game}, for multi-agent
reinforcement learning with a large state space and a large number of agents. This is a class …

We study a multi-agent reinforcement learning (MARL) problem where the agents interact
over a given network. The goal of the agents is to cooperatively maximize the average of …

This paper studies policy optimization algorithms for multi-agent reinforcement learning. We
begin by proposing an algorithm framework for two-player zero-sum Markov Games in the …

This work studies an independent natural policy gradient (NPG) algorithm for the multi-agent
reinforcement learning problem in Markov potential games. It is shown that, under mild …