M
Mykel J. Kochenderfer
Researcher at Stanford University
Publications - 449
Citations - 12534
Mykel J. Kochenderfer is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Markov decision process. The author has an hindex of 41, co-authored 388 publications receiving 8215 citations. Previous affiliations of Mykel J. Kochenderfer include Massachusetts Institute of Technology & University of Edinburgh.
Papers
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Journal ArticleDOI
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data
TL;DR: This paper develops a method for learning a probabilistic generative model of aircraft motion in terminal airspace, the controlled airspace surrounding a given airport, and finds that the model generates realistic trajectories, provides accurate predictions, and captures the statistical properties of the aircraft trajectories.
Proceedings ArticleDOI
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
TL;DR: This work proposes a modular decision making algorithm to autonomously navigate intersections, addressing challenges of existing rule-based and reinforcement learning (RL) approaches, and introduces a belief update technique using a learning based approach.
Journal ArticleDOI
Accounting for State Uncertainty in Collision Avoidance
TL;DR: In this article, a computationally efficient framework for accounting for state uncertainty based on dynamic programming is presented for handling state uncertainty in collision avoidance systems, which can significantly enhance safety and improve robustness to sensor error.
Proceedings ArticleDOI
Weighted Double Q-learning
Journal ArticleDOI
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
Kevin Carlberg,Antony Jameson,Mykel J. Kochenderfer,Jeremy Morton,Liqian Peng,Freddie D. Witherden +5 more
TL;DR: In this article, the authors proposed a hierarchical approach to reduce the number of degrees of freedom within each element of the high-order discretization by applying autoencoders from deep learning.