no code implementations • 3 Nov 2021 • John Mern, Kyle Hatch, Ryan Silva, Cameron Hickert, Tamim Sookoor, Mykel J. Kochenderfer
The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network.
no code implementations • 16 Sep 2021 • John Mern, Sidhart Krishnan, Anil Yildiz, Kyle Hatch, Mykel J. Kochenderfer
In this work, we propose a method to build predictable policy trees as surrogates for policies such as neural networks.
no code implementations • 9 Jun 2021 • John Mern, Kyle Hatch, Ryan Silva, Jeff Brush, Mykel J. Kochenderfer
Defending computer networks from cyber attack requires coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations.
no code implementations • 8 Jun 2021 • John Mern, Mykel J. Kochenderfer
Monte Carlo planners can often return sub-optimal actions, even if they are guaranteed to converge in the limit of infinite samples.
no code implementations • 3 Dec 2020 • Kyle Hatch, John Mern, Mykel Kochenderfer
In this work, we present an obstacle avoidance system for small UAVs that uses a monocular camera with a hybrid neural network and path planner controller.
1 code implementation • 7 Oct 2020 • John Mern, Anil Yildiz, Larry Bush, Tapan Mukerji, Mykel J. Kochenderfer
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces.
1 code implementation • 7 Oct 2020 • John Mern, Anil Yildiz, Zachary Sunberg, Tapan Mukerji, Mykel J. Kochenderfer
Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree.
no code implementations • 17 Jun 2020 • John Mern, Peter Morales, Mykel J. Kochenderfer
The proposed method defines a conditional distribution for each variable in a sequential process by conditioning the parameters of a normalizing flow with recurrent neural connections.
no code implementations • 19 Mar 2020 • John Mern, Dorsa Sadigh, Mykel J. Kochenderfer
We show that our proposed representation results in an input space that is a factor of $m!$ smaller for inputs of $m$ objects.
no code implementations • 7 May 2019 • John Mern, Dorsa Sadigh, Mykel Kochenderfer
Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge.
no code implementations • 10 Dec 2018 • John Mern, Kyle Julian, Rachael E. Tompa, Mykel J. Kochenderfer
A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft.
no code implementations • 20 Feb 2018 • John Mern, Jayesh K. Gupta, Mykel Kochenderfer
An optimal set of synapse weights may then be found for a given choice of ANN activation function and SNN neuron.