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Found 5 papers, 0 papers with code
DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace
Software development in the aerospace domain requires adhering to strict, high-quality standards.
gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks
Multiple studies have demonstrated that even modern DNNs are susceptible to adversarial inputs, and this risk must thus be measured and mitigated to allow the deployment of DNNs in critical settings.
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming
Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications.
Verifying Learning-Based Robotic Navigation Systems
Our work is the first to establish the usefulness of DNN verification in identifying and filtering out suboptimal DRL policies in real-world robots, and we believe that the methods presented here are applicable to a wide range of systems that incorporate deep-learning-based agents.
Scenario-Assisted Deep Reinforcement Learning
In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints.
