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

In this work, we study two-player zero-sum stochastic games and develop a variant of the
smoothed best-response learning dynamics that combines independent learning dynamics …

We introduce a class of networked Markov potential games where agents are associated
with nodes in a network. Each agent has its own local potential function, and the reward of …

We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored
into overlapping groups. Each group represents a hyperedge, forming a hypergraph over …

This paper studies a class of multi-agent reinforcement learning (MARL) problems where the
reward that an agent receives depends on the states of other agents, but the next state only …

We consider decentralized learning for zero-sum games, where players only see their payoff
information and are agnostic to actions and payoffs of the opponent. Previous works …

We present the first study on provably efficient randomized exploration in cooperative multi-
agent reinforcement learning (MARL). We propose a unified algorithm framework for …

This paper proposes a scalable distributed policy gradient method and proves its
convergence to near-optimal solution in multi-agent linear quadratic networked systems …

Independent learning (IL), despite being a popular approach in practice to achieve
scalability in large-scale multi-agent systems, usually lacks global convergence guarantees …