Who Is The Strongest Enemy? Towards Optimal And Efficient Evasion Attacks In Deep RL

Abstract

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms of both whether we can find the optimal attack and how efficiently we can find it. Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space. This paper introduces a novel attacking method to find the optimal attacks through collaboration between a designed function named "actor" and an RL-based learner named "director". The actor crafts state perturbations for a given policy perturbation direction, and the director learns to propose the best policy perturbation

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysun2021who

Related papers

• Optimal Attack And Defense For Reinforcement Learning (2023)6.34
• Robust Reinforcement Learning On State Observations With Learned Optimal Adversary (2021)0.00
• Attacking And Defending Deep Reinforcement Learning Policies (2022)0.00
• Adversarial Policies: Attacking Deep Reinforcement Learning (2019)0.00
• Efficient Adversarial Training Without Attacking: Worst-case-aware Robust Reinforcement Learning (2022)0.00
• Learning To Cope With Adversarial Attacks (2019)0.00
• Real-time Adversarial Perturbations Against Deep Reinforcement Learning Policies: Attacks And Defenses (2021)0.00
• Towards Robust Policy: Enhancing Offline Reinforcement Learning With Adversarial Attacks And Defenses (2024)3.58