Individuals exhibit theory of mind, attributing beliefs, intent, and mental states to others as
explanations of observed actions. Dennett's intentional stance offers an analogous …

We study reinforcement learning in mean-field games. To achieve the Nash equilibrium,
which consists of a policy and a mean-field state, existing algorithms require obtaining the …

We consider online reinforcement learning in Mean-Field Games (MFGs). Unlike traditional
approaches, we alleviate the need for a mean-field oracle by developing an algorithm that …

This paper studies two fundamental problems in regularized Graphon Mean-Field Games
(GMFGs). First, we establish the existence of a Nash Equilibrium (NE) of any $\lambda …

We propose a reinforcement learning algorithm for stationary mean-field games, where the
goal is to learn a pair of mean-field state and stationary policy that constitutes the Nash …

In open agent systems, the set of agents that are cooperating or competing changes over
time and in ways that are nontrivial to predict. For example, if collaborative robots were …

Recent renewed interest in multi-agent reinforcement learning (MARL) has generated an
impressive array of techniques that leverage deep RL, primarily actor-critic architectures …

This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where
multiple agents interact in the same environment and whose goal is to maximize the …

In multi-agent environments in which coordination is desirable, the history of play often
causes lock-in at sub-optimal outcomes. Notoriously, technologies with significant …

Recent renewed interest in multi-agent reinforcement learning (MARL) has generated an
impressive array of techniques that leverage deep reinforcement learning, primarily actor …