138 citations · 402 across the 159 of their papers we have counts for
9 papers · 1 filter
Hierarchical Planning for Resource Allocation in Emergency Response Systems
Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer +1
A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Typically, such problems are modeled as Markov (or semi-Markov) decision pr…
Uncertainty Aware Wildfire Management
Tina Diao, Samriddhi Singla, Ayan Mukhopadhyay +3
Recent wildfires in the United States have resulted in loss of life and billions of dollars, destroying countless structures and forests. Fighting wildfires is extremely complex. I…
Designing Emergency Response Pipelines : Lessons and Challenges
Ayan Mukhopadhyay, Geoffrey Pettet, Mykel Kochenderfer +1
Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities. Emergency response management comprises of several stages and sub-prob…
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
Raunak Bhattacharyya, Blake Wulfe, Derek Phillips +4
An open problem in autonomous vehicle safety validation is building reliable models of human driving behavior in simulation. This work presents an approach to learn neural driving…
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
Ayan Mukhopadhyay, Geoffrey Pettet, Sayyed Vazirizade +7
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM)…
Online Parameter Estimation for Human Driver Behavior Prediction
Raunak Bhattacharyya, Ransalu Senanayake, Kyle Brown +1
Driver models are invaluable for planning in autonomous vehicles as well as validating their safety in simulation. Highly parameterized black-box driver models are very expressive,…
Exchangeable Input Representations for Reinforcement Learning
John Mern, Dorsa Sadigh, Mykel J. Kochenderfer
Poor sample efficiency is a major limitation of deep reinforcement learning in many domains. This work presents an attention-based method to project neural network inputs into an e…
Learning Near Optimal Policies with Low Inherent Bellman Error
Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer +1
We study the exploration problem with approximate linear action-value functions in episodic reinforcement learning under the notion of low inherent Bellman error, a condition norma…
On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities
Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer +2
Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in th…