Search Results for author:
Found 12 papers, 2 papers with code
Autonomous Attack Mitigation for Industrial Control Systems
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.
Interpretable Local Tree Surrogate Policies
In this work, we propose a method to build predictable policy trees as surrogates for policies such as neural networks.
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration
Defending computer networks from cyber attack requires coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations.
Measurable Monte Carlo Search Error Bounds
Monte Carlo planners can often return sub-optimal actions, even if they are guaranteed to converge in the limit of infinite samples.
Obstacle Avoidance Using a Monocular Camera
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.
Improved POMDP Tree Search Planning with Prioritized Action Branching
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces.
Bayesian Optimized Monte Carlo Planning
Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree.
Towards Recurrent Autoregressive Flow Models
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.
Exchangeable Input Representations for Reinforcement Learning
We show that our proposed representation results in an input space that is a factor of $m!$ smaller for inputs of $m$ objects.
Object Exchangeability in Reinforcement Learning: Extended Abstract
Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge.
Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks
A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft.
Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures
An optimal set of synapse weights may then be found for a given choice of ANN activation function and SNN neuron.
