This paper investigates the problem of computing the equilibrium of competitive games,
which is often modeled as a constrained saddle-point optimization problem with probability …

In this article, we study the problem of multiple stochastic agents interacting in a dynamic
game scenario with continuous state and action spaces. We define a new notion of …

We study a general class of entropy-regularized multi-variate LQG mean field game (MFG)
systems in continuous time with K distinct subpopulations of agents. We extend the notion of …

In this paper, we study an entropy-regularized continuous-time linear-quadratic two-person
zero-sum stochastic differential game problem from the perspective of reinforcement …

Efficient computation of the optimal transport distance between two distributions serves as
an algorithm subroutine that empowers various applications. This paper develops a scalable …

We study optimal control in models with latent factors where the agent controls the
distribution over actions, rather than actions themselves, in both discrete and continuous …

In competitive multi-player interactions, simultaneous optimality is a key requirement for
establishing strategic equilibria. This property is explicit when the game-theoretic …

This paper addresses policy learning in non-stationary environments and games with
continuous actions. Rather than the classical reward maximization mechanism, inspired by …

Reinforcement learning (RL) aims to solve various tasks by modeling them as learning and
sequential decision-making problems within an unknown environment. The empirical …

This dissertation investigates two popular machine learning frameworks, namely, minimax
optimization and multiagent reinforcement learning (MARL). There are a large number of …