Repository for the paper "Detecting Anomalous Agent Decision Sequences Based on Offline Imitation Learning" (AAMAS 2024).
A link to the full version of this work can be found on arXiv.
Requires Python 3.8
pip install -r requirements.txt
To train the model on LunarLander dataset:
python train/model_training.py
(For the full list of arguments and defaults, see train/model_training.py)
To evaluate on LunarLander dataset:
python ad_eval.py
This dataset contains taxi trajectories within the Second Ring Road of Chengdu City in China. During the pre-processing step, we extract trajectories with the same order ID. We transform GPS data into state-action pairs in a discrete grid world (
This dataset contains sea vessel traffic data from sub-areas in Australia. During the pre-processing step, we extract trajectories that are from cargo and tanker vessels from July to December 2020 in the Bass Strait area. Each state includes latitude, longitude and speed. We discretize the course into five actions: moving north, moving east, moving south, moving west and staying stationary. This dataset has a continuous state space and discrete action space.
This dataset contains trajectories generated by a well-trained Proximal Policy Optimization (PPO) agent in the gym environment LunarLander-v2 that solves a classic rocket optimization problem. The average expected reward of each trajectory from the demonstrator is
Please cite our paper as:
@inproceedings{wang2024detecting,
title={Detecting Anomalous Agent Decision Sequences Based on Offline Imitation Learning},
author={Wang, Chen and Erfani, Sarah and Alpcan, Tansu and Leckie, Christopher},
booktitle={Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems},
pages={2543--2545},
year={2024}
}
or
@article{wang2024oil,
title={OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences},
author={Wang, Chen and Erfani, Sarah and Alpcan, Tansu and Leckie, Christopher},
journal={arXiv preprint arXiv:2402.04567},
year={2024}
}