Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks (1701.04143v1)

Abstract: Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.

• The paper introduces policy induction attacks that exploit adversarial perturbations to manipulate Deep Q-Network learning policies.
• It validates the attack mechanism using a Pong game scenario, demonstrating the transferability of adversarial examples between models.
• The findings emphasize the need for novel defense strategies as conventional methods fail to secure deep reinforcement learning systems.

An Analysis of Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

This paper investigates the susceptibility of Deep Reinforcement Learning (RL) frameworks, specifically those based on Deep Q-Networks (DQNs), to policy induction attacks. The authors, Vahid Behzadan and Arslan Munir, have delved into the implications of adversarial input perturbations, a known vulnerability in deep learning classifiers, on reinforcement learning models. Their paper not only verifies that DQNs share this vulnerability but also presents a novel class of attacks that exploit this weakness to manipulate the learning policies of such networks.

The paper's primary contribution is the introduction of policy induction attacks, a form of adversarial strategy aimed at directing the learning process of DQNs towards a specific, potentially malicious policy. These attacks leverage the transferability of adversarial examples—a phenomenon where adversarially perturbed inputs to one neural network model also cause misclassification in another, unrelated model performing the same task. The authors propose a detailed attack mechanism, which they validate through an experimental setup involving a game-learning scenario, demonstrating the vulnerability of the DQNs.

The authors begin by providing context to the paper, underlining the integration of deep learning with reinforcement learning as an advancement that addresses classic RL systems' limitations in handling complex, high-dimensional spaces. However, with these advancements come new challenges, particularly regarding the robustness and integrity of RL applications in critical systems. The potential manipulation of an agent's learned policies through adversarial disturbance in its environment raises significant concerns, especially for systems reliant on robust control policies like autonomous navigation and robotic manipulations.

The experimental verification executed by the authors is comprehensive. By adopting a game scenario involving the Atari 2600 Pong environment, the paper confirms that DQNs are as susceptible to adversarial attacks as traditional deep neural network classifiers. Furthermore, the paper highlights strong empirical evidence of the transferability of adversarial examples between different Q-network models. Such results are crucial, demonstrating that even if the attack targets a model clone rather than the exact original, policy manipulation remains feasible.

A critical observation from the paper is the insufficiency of current defense mechanisms against adversarial attacks, such as adversarial training or defensive distillation. These conventional countermeasures seem ineffective in shielding DQNs from policy induction attacks, thereby opening up an area that requires further exploration. The authors postulate that counteracting such vulnerabilities might necessitate new strategies that can dynamically adapt the exploration-exploitation mechanisms of DQNs or incorporate advanced pattern recognition to filter out potential perturbations preemptively.

In conclusion, this paper serves as a seminal work exploring the intersection of deep reinforcement learning and adversarial attack strategies. It extends the understanding of deep networks' vulnerabilities in a reinforcement learning context, urging more nuanced approaches to securing these systems against adversarial influences. Future work, as inferred from the paper, could focus on theoretical models to understand the bounds and parameters influencing DQN susceptibility, as well as the development of robust, adaptive mechanisms to fortify deep RL frameworks against potential threats. The ongoing evolution of AI necessitates guarding against its vulnerabilities, ensuring the reliability and safety of AI-driven systems in critical applications.