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Regret-Based Defense in Adversarial Reinforcement Learning: A Comprehensive Analysis


Core Concepts
Optimizing regret in adversarial RL enhances robustness against observation attacks.
Abstract
This content delves into the concept of regret-based defense in adversarial reinforcement learning. It explores the vulnerabilities of deep reinforcement learning policies to adversarial noise in observations and proposes methods to enhance robustness. The analysis covers the importance of regret optimization, the formulation of regret-based defense approaches, and the comparison of these approaches with existing methods through empirical results on various benchmarks. Abstract: Deep Reinforcement Learning (DRL) policies are susceptible to adversarial noise in observations. Existing approaches focus on regularization and maximin notions of robustness. This study introduces regret-based defense to optimize robustness against adversarial attacks. Introduction: DRL models excel in complex tasks but are vulnerable to attacks on input. Adversarial perturbations can lead to catastrophic consequences in safety-critical environments. The study aims to develop inherently robust algorithms to counter observation-perturbing adversaries. Regret-Based Adversarial Defense (RAD): Defines regret and introduces Cumulative Contradictory Expected Regret (CCER) for scalable optimization. Proposes RAD-DRN using value iteration and RAD-PPO using policy gradients to minimize CCER. Introduces RAD-CHT, a cognitive hierarchy theory-based approach for adversary-reactive frameworks. Experimental Results: Evaluates RAD approaches against leading methods on MuJoCo, Atari, and Highway benchmarks. Demonstrates superior robustness of RAD methods against various attacks, including strategic adversaries. Compares performance degradation of approaches under increasing attack intensity.
Stats
Deep Reinforcement Learning (DRL) policies are vulnerable to adversarial noise in observations. Regularization approaches aim to make expected value objectives robust by adding adversarial loss terms. Maximin objectives focus on maximizing the minimum value for robustness. The study introduces regret-based defense to optimize robustness against adversarial attacks.
Quotes
"We focus on optimizing a well-studied robustness objective, namely regret." "Our methods outperform existing best approaches for adversarial RL problems across a variety of standard benchmarks."

Key Insights Distilled From

by Roman Belair... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2302.06912.pdf
Regret-Based Defense in Adversarial Reinforcement Learning

Deeper Inquiries

How can regret-based defense be applied to other domains beyond reinforcement learning

Regret-based defense can be applied to other domains beyond reinforcement learning by adapting the concept of regret to suit the specific characteristics of those domains. In the context of adversarial settings, regret can be defined as the difference in outcomes between taking an action under normal conditions and taking the same action under adversarial conditions. This concept can be extended to domains such as natural language processing, computer vision, and cybersecurity. For example, in natural language processing, regret-based defense could involve minimizing the impact of adversarial inputs on language models by optimizing for regret in text generation or classification tasks. Similarly, in computer vision, regret-based defense could focus on reducing the vulnerability of image recognition systems to adversarial attacks by optimizing for regret in image classification tasks. By customizing the regret framework to the specific requirements of different domains, it can serve as a powerful tool for enhancing robustness and security in various AI applications.

What are the potential drawbacks or limitations of regret-based defense in adversarial settings

While regret-based defense offers significant advantages in adversarial settings, there are potential drawbacks and limitations to consider. One limitation is the computational complexity involved in optimizing regret, especially in scenarios with high-dimensional action spaces or complex environments. The iterative nature of regret optimization may require substantial computational resources and time, making it less practical for real-time applications or large-scale systems. Additionally, regret-based defense may struggle with generalization to unseen adversarial strategies, as it primarily focuses on minimizing regret for known attack patterns. This lack of adaptability to novel attacks could limit the effectiveness of regret-based approaches in dynamic and evolving threat landscapes. Furthermore, the conservative nature of regret optimization may lead to overly cautious decision-making, potentially sacrificing performance in pursuit of robustness. Balancing robustness and performance remains a key challenge in implementing regret-based defense in adversarial settings.

How can regret optimization impact the broader field of machine learning and artificial intelligence

Regret optimization has the potential to impact the broader field of machine learning and artificial intelligence by offering a principled approach to enhancing robustness and security in AI systems. By incorporating regret into the training and decision-making processes of machine learning models, researchers and practitioners can develop more resilient and reliable AI solutions. In the context of reinforcement learning, regret-based defense can lead to the development of agents that are better equipped to handle adversarial attacks and unexpected perturbations in their observations. This can have significant implications for applications in autonomous systems, cybersecurity, and finance, where robustness is crucial. Furthermore, the principles of regret optimization can be extended to other areas of machine learning, such as supervised learning and unsupervised learning, to improve model performance under adversarial conditions. Overall, integrating regret-based defense techniques into AI systems can contribute to the advancement of trustworthy and secure artificial intelligence technologies.
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