Summary of Rat: Adversarial Attacks on Deep Reinforcement Agents For Targeted Behaviors, by Fengshuo Bai et al.
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors
by Fengshuo Bai, Runze Liu, Yali Du, Ying Wen, Yaodong Yang
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel method called RAT (Robust Adversarial Targeting) for evaluating deep reinforcement learning (DRL) agents against targeted behavior attacks. The authors highlight the importance of assessing DRL agent robustness by manipulating their behavior to align with specific human preferences. The proposed method trains an intention policy that is explicitly aligned with human preferences, serving as a precise behavioral target for the adversary. The adversary then manipulates the victim’s policy to follow this target behavior. To enhance the effectiveness of these attacks, RAT dynamically adjusts the state occupancy measure within the replay buffer. The empirical results on robotic simulation tasks demonstrate that RAT outperforms existing adversarial attack algorithms in inducing specific behaviors and improving agent robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAT is a new way to test how well artificial intelligence (AI) agents can work together safely. Right now, AI agents are being trained to do certain things, like move robots or make decisions. But what if someone tries to trick these agents into doing something else? The authors of this paper came up with a plan called RAT to make sure AI agents don’t get fooled. They want to see if an AI agent can be convinced to do something specific that aligns with human preferences. This is important because it could help make AI agents safer and more reliable. |
Keywords
» Artificial intelligence » Reinforcement learning
