Cooperative automated vehicles: A review of opportunities and challenges in socially intelligent vehicles beyond networking
SW Loke - IEEE Transactions on Intelligent Vehicles, 2019 - ieeexplore.ieee.org
The connected automated vehicle has been often touted as a technology that will become
pervasive in society in the near future. One can view an automated vehicle as having …
pervasive in society in the near future. One can view an automated vehicle as having …
Tight last-iterate convergence rates for no-regret learning in multi-player games
We study the question of obtaining last-iterate convergence rates for no-regret learning
algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with a …
algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with a …
Finite-time last-iterate convergence for multi-agent learning in games
In this paper, we consider multi-agent learning via online gradient descent in a class of
games called $\lambda $-cocoercive games, a fairly broad class of games that admits many …
games called $\lambda $-cocoercive games, a fairly broad class of games that admits many …
Mechanism design theory in control engineering: A tutorial and overview of applications in communication, power grid, transportation, and security systems
This article provides an introduction to the theory of mechanism design and its application to
engineering problems. Our aim is to provide the fundamental principles of mechanism …
engineering problems. Our aim is to provide the fundamental principles of mechanism …
Learning how to dynamically route autonomous vehicles on shared roads
Road congestion induces significant costs across the world, and road network disturbances,
such as traffic accidents, can cause highly congested traffic patterns. If a planner had control …
such as traffic accidents, can cause highly congested traffic patterns. If a planner had control …
Mirror descent learning in continuous games
Online Mirror Descent (OMD) is an important and widely used class of adaptive learning
algorithms that enjoys good regret performance guarantees. It is therefore natural to study …
algorithms that enjoys good regret performance guarantees. It is therefore natural to study …
Data-driven distributionally robust optimization for vehicle balancing of mobility-on-demand systems
With the transformation to smarter cities and the development of technologies, a large
amount of data is collected from sensors in real time. Services provided by ride-sharing …
amount of data is collected from sensors in real time. Services provided by ride-sharing …
Data-driven distributionally robust vehicle balancing using dynamic region partitions
With the transformation to smarter cities and the development of technologies, a large
amount of data is collected from sensors in real-time. This paradigm provides opportunities …
amount of data is collected from sensors in real-time. This paradigm provides opportunities …
Countering feedback delays in multi-agent learning
We consider a model of game-theoretic learning based on online mirror descent (OMD) with
asynchronous and delayed feedback information. Instead of focusing on specific games, we …
asynchronous and delayed feedback information. Instead of focusing on specific games, we …
Learning in games with lossy feedback
We consider a game-theoretical multi-agent learning problem where the feedback
information can be lost during the learning process and rewards are given by a broad class …
information can be lost during the learning process and rewards are given by a broad class …