Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Significant advances have recently been achieved in Multi-Agent Reinforcement Learning
(MARL) which tackles sequential decision-making problems involving multiple participants …

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 …

This paper considers offline multi-agent reinforcement learning. We propose the strategy-
wise concentration principle which directly builds a confidence interval for the joint strategy …

This paper considers multi-agent reinforcement learning (MARL) where the rewards are
received after delays and the delay time varies across agents and across time steps. Based …

This paper investigates when one can efficiently recover an approximate Nash Equilibrium
(NE) in offline congestion games. The existing dataset coverage assumption in offline …

The increasing prevalence of multi-agent learning systems in society necessitates
understanding how to learn effective and safe policies in general-sum multi-agent …

In control theory, typically a nominal model is assumed based on which an optimal control is
designed and then applied to an actual (true) system. This gives rise to the problem of …

As a large and ever-increasing part of our economic and social interactions moves to the
cyberspace, data-driven algorithmic decision making by autonomous agents is fast …

Reinforcement learning has been proven to be an effective approach for solving multi-agent
coordination problems in a dynamic open environment. For dealing with multi-agent …