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

Since the inception of Deep Reinforcement Learning (DRL) algorithms, therehas been a growing interest in both research and industrial communities in thepromising potentials of this paradigm. The list of current and envisionedapplications of deep RL ranges from autonomous navigation and robotics tocontrol applications in the critical infrastructure, air traffic control,defense technologies, and cybersecurity. While the landscape of opportunitiesand the advantages of deep RL algorithms are justifiably vast, the securityrisks and issues in such algorithms remain largely unexplored. To facilitateand motivate further research on these critical challenges, this paper presentsa foundational treatment of the security problem in DRL. We formulate thesecurity requirements of DRL, and provide a high-level threat model through theclassification and identification of vulnerabilities, attack vectors, andadversarial capabilities. Furthermore, we present a review of currentliterature on security of deep RL from both offensive and defensiveperspectives. Lastly, we enumerate critical research venues and open problemsin mitigation and prevention of intentional attacks against deep RL as aroadmap for further research in this area.

The Faults in Our Pi Stars: Security Issues and Open Challenges in Deep Reinforcement Learning