Query-based Targeted Action-space Adversarial Policies On Deep Reinforcement Learning Agents

Abstract

Advances in computing resources have resulted in the increasing complexity of cyber-physical systems (CPS). As the complexity of CPS evolved, the focus has shifted from traditional control methods to deep reinforcement learning-based (DRL) methods for control of these systems. This is due to the difficulty of obtaining accurate models of complex CPS for traditional control. However, to securely deploy DRL in production, it is essential to examine the weaknesses of DRL-based controllers (policies) towards malicious attacks from all angles. In this work, we investigate targeted attacks in the action-space domain, also commonly known as actuation attacks in CPS literature, which perturbs the outputs of a controller. We show that a query-based black-box attack model that generates optimal perturbations with respect to an adversarial goal can be formulated as another reinforcement learning problem. Thus, such an adversarial policy can be trained using conventional DRL methods. Experimental

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keylee2020query

Related papers

• Adversarial Policies: Attacking Deep Reinforcement Learning (2019)0.00
• Real-time Adversarial Perturbations Against Deep Reinforcement Learning Policies: Attacks And Defenses (2021)0.00
• Online Robust Policy Learning In The Presence Of Unknown Adversaries (2018)0.00
• Attacking And Defending Deep Reinforcement Learning Policies (2022)0.00
• RAT: Adversarial Attacks On Deep Reinforcement Agents For Targeted Behaviors (2024)0.00
• Robust Deep Reinforcement Learning Against Adversarial Behavior Manipulation (2024)0.00
• Targeted Adversarial Attacks On Deep Reinforcement Learning Policies Via Model Checking (2022)2.26
• Neutral Agent-based Adversarial Policy Learning Against Deep Reinforcement Learning In Multi-party Open Systems (2025)0.00