Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control
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Abstract (arXiv)
Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents to adversarial attacks is studied. In particular, we consider multiple DRL agents that perform both dynamic channel access and power control in wireless interference channels. For these victim DRL agents, we design a jammer, which is also a DRL agent. We propose an adversarial jamming attack scheme that utilizes a listening phase and significantly degrades the users' sum rate. Subsequently, we develop an ensemble policy defense strategy against such a jamming attacker by reloading models (saved during retraining) that have minimum transition correlation.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 12 May 2021.
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22 of 39 references.
References · 22
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- 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 225
- 2019Adversarial Policies: Attacking Deep Reinforcement Learningcited by 214
- 2017Tactics of Adversarial Attack on Deep Reinforcement Learning Agentscited by 166
10 more
- 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
- 2019Adversarial Attacks on Deep Neural Networks for Time Series Classificationcited by 53
- 2018Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networkscited by 37
- 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
- 2019Characterizing Attacks on Deep Reinforcement Learningcited by 31
- 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
- 2019Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Informationcited by 13
- 2020When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directionscited by 12
- 2019Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reductioncited by 4
- 2020Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learningcited by 2
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