Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning
(MARL) paradigms implementing a centralized learning with decentralized execution …

In TD-learning, off-policy sampling is known to be more practical than on-policy sampling,
and by decoupling learning from data collection, it enables data reuse. It is known that policy …

The writing sequence of numerals or letters often affects aesthetic aspects of the writing
outcomes. As such, it remains a challenge for robotic calligraphy systems to perform …

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 …

We investigate Nash equilibrium learning in a competitive Markov Game (MG) environment,
where multiple agents compete, and multiple Nash equilibria can exist. In particular, for an …

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

Mean-field games (MFG) were introduced to efficiently analyze approximate Nash equilibria
in large population settings. In this work, we consider entropy-regularized mean-field games …

Coordinated all campus activities in the area of autonomy. Administered the IRIM Seed
Grants program, established and administered the IRIM Visiting Faculty and Resident …