Abstract
Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents—from self-driving cars, ships to
aerial drones—that promise to radically improve
productivity and safety. Achieving coordination
among agents in such urban settings presents several algorithmic challenges—ability to scale to
thousands of agents, addressing uncertainty, and
partial observability in the environment. In addition, accurate domain models need to be learned
from data that is often noisy and available only at
an aggregate level. In this paper, I will overview
some of our recent contributions towards developing planning and reinforcement learning strategies
to address several such challenges present in largescale urban multiagent systems