15 citations · 27 across the 10 of their papers we have counts for
13 papers
How does a Rational Agent Act in an Epidemic?
S. Yagiz Olmez, Shubham Aggarwal, Jin Won Kim +4
Evolution of disease in a large population is a function of the top-down policy measures from a centralized planner, as well as the self-interested decisions (to be socially active…
Modeling Presymptomatic Spread in Epidemics via Mean-Field Games
S. Yagiz Olmez, Shubham Aggarwal, Jin Won Kim +4
This paper is concerned with developing mean-field game models for the evolution of epidemics. Specifically, an agent's decision -- to be socially active in the midst of an epidemi…
Discrete-Time Linear-Quadratic Regulation via Optimal Transport
Mathias Hudoba de Badyn, Erik Miehling, Dylan Janak +5
In this paper, we consider a discrete-time stochastic control problem with uncertain initial and target states. We first discuss the connection between optimal transport and stocha…
Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games
Muhammad Aneeq uz Zaman, Kaiqing Zhang, Erik Miehling +1
In this paper, we study large population multi-agent reinforcement learning (RL) in the context of discrete-time linear-quadratic mean-field games (LQ-MFGs). Our setting differs fr…
Approximate Equilibrium Computation for Discrete-Time Linear-Quadratic Mean-Field Games
Muhammad Aneeq uz Zaman, Kaiqing Zhang, Erik Miehling +1
While the topic of mean-field games (MFGs) has a relatively long history, heretofore there has been limited work concerning algorithms for the computation of equilibrium control po…
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning
Weichao Mao, Kaiqing Zhang, Erik Miehling +1
Multi-agent reinforcement learning (MARL) under partial observability has long been considered challenging, primarily due to the requirement for each agent to maintain a belief ove…
Protecting Consumers Against Personalized Pricing: A Stopping Time Approach
Roy Dong, Erik Miehling, Cedric Langbort
The widespread availability of behavioral data has led to the development of data-driven personalized pricing algorithms: sellers attempt to maximize their revenue by estimating th…
Non-Cooperative Inverse Reinforcement Learning
Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling +1
Making decisions in the presence of a strategic opponent requires one to take into account the opponent's ability to actively mask its intended objective. To describe such strategi…
Strategic Inference with a Single Private Sample
Erik Miehling, Roy Dong, Cédric Langbort +1
Motivated by applications in cyber security, we develop a simple game model for describing how a learning agent's private information influences an observing agent's inference proc…
Online Planning for Decentralized Stochastic Control with Partial History Sharing
Kaiqing Zhang, Erik Miehling, Tamer Başar
In decentralized stochastic control, standard approaches for sequential decision-making, e.g. dynamic programming, quickly become intractable due to the need to maintain a complex…
A Decentralized Mechanism for Computing Competitive Equilibria in Deregulated Electricity Markets
Erik Miehling, Demosthenis Teneketzis
With the increased level of distributed generation and demand response comes the need for associated mechanisms that can perform well in the face of increasingly complex deregulate…
A Supervisory Control Approach to Dynamic Cyber-Security
Mohammad Rasouli, Erik Miehling, Demosthenis Teneketzis
An analytical approach for a dynamic cyber-security problem that captures progressive attacks to a computer network is presented. We formulate the dynamic security problem from the…
Sequential Detection with Mutual Information Stopping Cost
Vikram Krishnamurthy, Robert Bitmead, Michel Gevers +1
This paper formulates and solves a sequential detection problem that involves the mutual information (stochastic observability) of a Gaussian process observed in noise with missing…