https://github.com/sisl/overtverify.jl

https://github.com/sisl/overtverify.jl

Last synced: 9 months ago
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• Host: GitHub
• URL: https://github.com/sisl/overtverify.jl
• Owner: sisl
• License: mit
• Created: 2021-07-26T22:15:51.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2025-01-15T23:36:25.000Z (over 1 year ago)
• Last Synced: 2025-01-16T00:52:42.002Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 9.38 MB
• Stars: 8
• Watchers: 8
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# OVERTVerify

[![CI](https://github.com/sisl/OVERTVerify.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/sisl/OVERTVerify.jl/actions/workflows/CI.yml)

[![codecov](https://codecov.io/gh/sisl/OVERTVerify.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/sisl/OVERTVerify.jl)

This repo integrates OVERT with reachability functionality for the purpose of verifying closed-loop systems with neural network control policies.

[OVERT](https://github.com/sisl/OVERT.jl) is a `julia` package that provides a relational piecewise-linear overapproximation of any multi-dimensional function. 

Such an overapproximation is used for converting smooth nonlinear functions that commonly appear in closed-loop dynamical systems into a set of relations with piecewise-linear functions such as `min` and `max`. 

The overapproximated dynamics, together with the neural network control policy (which is assumed to be ReLU-activated) is then represented as a mixed integer program using an encoding inspired by the [MIPVerify algorithm](https://arxiv.org/abs/1711.07356).

This system is unrolled in time and used to answer bounded-time reachability queries such as: can the closed loop system avoid an unsafe set? Or reach a goal set?

## Dependencies

By default, this package will use GLPK as it is open-source. However, Gurobi tends to perform better and was used for the experiments in the paper. If you have or can obtain a Gurobi license, it is recommended to use Gurobi. An academic Gurobi license can be acquired for free here: 

https://www.gurobi.com/academia/academic-program-and-licenses/

The Julia dependencies are listed in the Project.toml file and will be automatically installed when you add the package (see Installation below).

## Installation

```

] add https://github.com/sisl/OVERTVerify.jl

```

## Usage

See the documentation [here](docs/src/index.md)

### Citation

```

@article{sidrane2022overt,

  title={OVERT: An algorithm for safety verification of neural network control policies for nonlinear systems},

  author={Sidrane, Chelsea and Maleki, Amir and Irfan, Ahmed and Kochenderfer, Mykel J},

  journal={Journal of Machine Learning Research},

  volume={23},

  number={117},

  pages={1--45},

  year={2022}

}

```