activity
20192021
most citedNon-Cooperative Inverse Reinforcement Learning

15 citations · 16 across the 4 of their papers we have counts for

collaborators

8 papers

math.OC2021

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…

eess.SY2020

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…

eess.SY2020

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…

cs.AI2020

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…

cs.CE20201 cited

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…

cs.GT201915 cited

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…