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Found 12 papers, 4 papers with code
Analyzing Adversarial Inputs in Deep Reinforcement Learning
In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems.
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.
Formally Explaining Neural Networks within Reactive Systems
We evaluate our approach on two popular benchmarks from the domain of automated navigation; and observe that our methods allow the efficient computation of minimal and minimum explanations, significantly outperforming the state of the art.
Verifying Generalization in Deep Learning
Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the state of the art in numerous application domains.
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System
In this short paper, we present our ongoing work on the veriFIRE project -- a collaboration between industry and academia, aimed at using verification for increasing the reliability of a real-world, safety-critical system.
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.
Verification-Aided Deep Ensemble Selection
Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex tasks.
Towards Scalable Verification of Deep Reinforcement Learning
Deep neural networks (DNNs) have gained significant popularity in recent years, becoming the state of the art in a variety of domains.
Neural Network Robustness as a Verification Property: A Principled Case Study
Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields.
An SMT-Based Approach for Verifying Binarized Neural Networks
One novelty of our technique is that it allows the verification of neural networks that include both binarized and non-binarized components.
