Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control

• URL: http://arxiv.org/abs/2105.05817v1
• Date: Wed, 12 May 2021 17:27:21 GMT
• Title: Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control
• Authors: Feng Wang, M. Cenk Gursoy, and Senem Velipasalar
• Abstract summary: Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. We consider multiple DRL agents that perform both dynamic channel access and power control in wireless interference channels. We propose an adversarial jamming attack scheme that utilizes a listening phase and significantly degrades the users' sum rate.
• Score: 13.619849476923877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning [29.868059421372244]
  Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, but its algorithmic robustness against adversarial perturbations remains unexplored. We first propose the Semi-Contrastive Representation attack, inspired by the adversarial contrastive attack. We then introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer.
  arXiv  Detail & Related papers  (2023-12-12T16:05:55Z)
• Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning [0.0]
  "DManD-RL" (data-driven manifold dynamics-RL) generates a data-driven low-dimensional model of our system. We train an RL control agent, yielding a 440-fold speedup over training on a numerical simulation. The agent learns a policy that laminarizes 84% of unseen DNS test trajectories within 900 time units.
  arXiv  Detail & Related papers  (2023-01-28T05:47:10Z)
• Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks [21.618559590818236]
  We discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms.
  arXiv  Detail & Related papers  (2022-09-16T04:50:49Z)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training [71.7750435554693]
  We show that several state-of-the-art RL agents proposed for power system control are vulnerable to adversarial attacks. Specifically, we use an adversary Markov Decision Process to learn an attack policy, and demonstrate the potency of our attack. We propose to use adversarial training to increase the robustness of RL agent against attacks and avoid infeasible operational decisions.
  arXiv  Detail & Related papers  (2021-10-18T00:50:34Z)
• Robust Reinforcement Learning on State Observations with Learned Optimal Adversary [86.0846119254031]
  We study the robustness of reinforcement learning with adversarially perturbed state observations. With a fixed agent policy, we demonstrate that an optimal adversary to perturb state observations can be found. For DRL settings, this leads to a novel empirical adversarial attack to RL agents via a learned adversary that is much stronger than previous ones.
  arXiv  Detail & Related papers  (2021-01-21T05:38:52Z)
• Robust Deep Reinforcement Learning through Adversarial Loss [74.20501663956604]
  Recent studies have shown that deep reinforcement learning agents are vulnerable to small adversarial perturbations on the agent's inputs. We propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2020-08-05T07:49:42Z)
• Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning [12.11027948206573]
  In this paper, we consider a victim user that performs DRL-based dynamic channel access, and an attacker that executes DRLbased jamming attacks to disrupt the victim. Both the victim and attacker are DRL agents and can interact with each other, retrain their models, and adapt to opponents' policies. We propose three defense strategies to maximize the attacked victim's accuracy and evaluate their performances.
  arXiv  Detail & Related papers  (2020-07-12T18:16:00Z)
• Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations [88.94162416324505]
  A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations deviate from the true states, they can mislead the agent into making suboptimal actions. We show that naively applying existing techniques on improving robustness for classification tasks, like adversarial training, is ineffective for many RL tasks.
  arXiv  Detail & Related papers  (2020-03-19T17:59:59Z)
• Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning [48.49658986576776]
  Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications. This paper presents emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks.
  arXiv  Detail & Related papers  (2020-01-27T10:53:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.