English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Adversarial examples are firstly investigated in the area of computer vision:by adding some carefully designed ''noise'' to the original input image, theperturbed image that cannot be distinguished from the original one by human,can fool a well-trained classifier easily. In recent years, researchers alsodemonstrated that adversarial examples can mislead deep reinforcement learning(DRL) agents on playing video games using image inputs with similar methods.However, although DRL has been more and more popular in the area of intelligenttransportation systems, there is little research investigating the impacts ofadversarial attacks on them, especially for algorithms that do not take imagesas inputs. In this work, we investigated several fast methods to generateadversarial examples to significantly degrade the performance of a well-trainedDRL- based energy management system of an extended range electric deliveryvehicle. The perturbed inputs are low-dimensional state representations andclose to the original inputs quantified by different kinds of norms. Our workshows that, to apply DRL agents on real-world transportation systems,adversarial examples in the form of cyber-attack should be consideredcarefully, especially for applications that may lead to serious safety issues.

Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles