https://github.com/mitre/arlin

ARLIN is a research library written in Python that provides explainability outputs and vulnerability detection for DRL models, specifically designed to increase model assurance and identify vulnerabilities and potential points of failure within a trained model. https://mitre.github.io/arlin/
https://github.com/mitre/arlin

adversarial-machine-learning explainable-ai python reinforcement-learning

Last synced: about 1 year ago
JSON representation

Host: GitHub
URL: https://github.com/mitre/arlin
Owner: mitre
License: other
Created: 2023-11-08T13:57:45.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-02-06T13:53:16.000Z (over 2 years ago)
Last Synced: 2025-04-01T14:45:41.112Z (about 1 year ago)
Topics: adversarial-machine-learning, explainable-ai, python, reinforcement-learning
Language: Python
Homepage:
Size: 13.3 MB
Stars: 11
Watchers: 6
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# ARLIN - Assured Reinforcement Learning Model Interrogation

ARLIN is a Python library that provides explainability outputs for trained Reinforcement
Learning (RL) models that can be used to identify potential policy vulnerabilities and
critical points. Using XRL datasets, ARLIN provides detailed analysis into an RL model’s
latent space, creates a semi-aggregated Markov decision process (SAMDP) to outline the
model’s path throughout an episode, and produces cluster analytics for each node within
the SAMDP to identify potential failure points and vulnerabilities within the model.

Documentation is available [here](https://mitre.github.io/arlin/).

**Table of Contents**

- [About ARLIN](#about-arlin)
- [Main Features](#main-features)
- [Installation](#installation)
- [Getting Started](#getting-started)
- [License](#license)
- [Citation](#citation)

# About ARLIN
The ARLIN Toolkit is a research library written in Python that provides explainability
outputs and vulnerability detection for Deep Reinforcement Learning (DRL) models,
specifically designed to increase model assurance and identify potential points of
failure within a trained model. ARLIN utilizes [matplotlib](https://matplotlib.org) and
[networkx](https://networkx.org) to visualize a trained RL model’s decision making
process and provide meaningful vulnerability identification and analysis to researchers.
The modular library is structured to easily support custom architecture, algorithm,
framework, and analytics modifications and provides a well-documented and tested API for
XRL research development and model assurance.

Key functionalities of the ARLIN library include:
- Creation of an XRL dataset with user-defined datapoints from a trained policy with the
ability to support custom data, algorithms, and model architectures.
- Dimensionality reduction and embedding generation of a trained model's latent space.
- Unsupervised clustering of policy latent space outputs based on policy transition data
and available XRL attributes.
- Analysis and visualization of policy latent space embeddings and clusters.
- Semi-aggregated Markov decision process (SAMDP) generation and policy-specific path
analysis.

# Main Features

At a high-level, ARLIN has 4 main components: `dataset`, `generation`, `analysis`, and
`SAMDP`. The `dataset` component is used to create an XRL dataset from a trained model.
`generation` uses the XRL dataset to create embeddings and clusters, which `analysis`
provides meaningful analysis and visualizations of. The cluster data and XRL dataset can
also be provided to `SAMDP` to generate and visualize different SAMDP graphs of the
agent's policy along with available paths between given clusters.

# Installation

*Note: ARLIN has only been tested on Ubuntu 18.04 and Python 3.11.6

1. **Clone the repository**

```bash
git clone https://gitlab.mitre.org/advxai/arlin.git
```

2. **Setup Pyenv Virtual Environment**

```bash
curl https://pyenv.run | bash
```

```bash
export PYENV_ROOT="$HOME/.pyenv"
command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"
```

```bash
pyenv install 3.11.6
pyenv local 3.11.6
```

3. **Install poetry**

```bash
curl -sSL https://install.python-poetry.org | python3 -
```

*Note: Don't forget to add `poetry` to your path.*
```bash
export PATH="$HOME/.local/bin:$PATH"
```

```bash
poetry config virtualenvs.in-project true
poetry env use 3.11.6
```

4. **Install required packages**

```bash
cd arlin
poetry shell
poetry install --no-dev
```

*Note: To re-enter the environment after this step, run `poetry shell`.*

# Getting Started

ARLIN provides a number of example usage notebooks within the [examples](./examples/)
directory. Documentation is available [here](https://mitre.github.io/arlin/).

# License

ARLIN is released under the [Apache 2.0 license](LICENSE).

# Citation

If you use `ARLIN` for your work, please cite the following BibTex entry.

```BibTeX
@misc{tapley2023arlin,
author = {Alexander Tapley},
title = {ARLIN},
howpublished = {\url{https://github.com/mitre/arlin}},
year = {2023}
}
```

This project contains content developed by The MITRE Corporation. If this code is used in
a deployment or embedded within another project, it is requested that you send an email
to opensource@mitre.org in order to let us know where this software is being used.

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/mitre/arlin

Awesome Lists containing this project

README