Multi-agent reinforcement learning has made substantial empirical progresses in solving
games with a large number of players. However, theoretically, the best known sample …

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 …

We study the problem of online learning in a two-player decentralized cooperative
Stackelberg game. In each round, the leader first takes an action, followed by the follower …

We study the hidden-action principal-agent problem in an online setting. In each round, the
principal posts a contract that specifies the payment to the agent based on each outcome …

This paper considers two-player zero-sum finite-horizon Markov games with simultaneous
moves. The study focuses on the challenging settings where the value function or the model …

We study a bilevel economic system, which we refer to as a Markov exchange economy
(MEE), from the point of view of multi-agent reinforcement learning (MARL). An MEE …

In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

We present a novel unified bilevel optimization-based framework,\textsf {PARL}, formulated
to address the recently highlighted critical issue of policy alignment in reinforcement …

We study multi-player general-sum Markov games with one of the players designated as the
leader and the other players regarded as followers. In particular, we focus on the class of …

We introduce a new approach for computing optimal equilibria via learning in games. It
applies to extensive-form settings with any number of players, including mechanism design …