THE IMPACT OF ONLINE SHIELDING FOR REINFORCEMENT LEARNING

THE IMPACT OF ONLINE SHIELDING FOR REINFORCEMENT LEARNING  

Abstract:
Reinforcement learning (RL) has emerged as a powerful approach for training intelligent agents to make optimal decisions in complex and dynamic environments. However, RL algorithms often face challenges when deployed in real-world scenarios due to their vulnerability to adversarial attacks. Adversarial attacks can manipulate the input data or the learning process itself, leading to suboptimal or even harmful agent behavior.

To address these security concerns, researchers have proposed various defense mechanisms, one of which is online shielding. Online shielding is a technique that aims to protect RL agents from adversarial attacks by dynamically modifying the input data during training or inference to remove or mitigate the impact of adversarial perturbations.

This paper investigates the impact of online shielding techniques on reinforcement learning performance. We first provide an overview of common adversarial attacks against RL agents and discuss their potential consequences. We then review different online shielding approaches, including input preprocessing techniques, reward shaping, and policy modification methods.

We conduct a comprehensive empirical analysis using state-of-the-art RL algorithms on benchmark environments to evaluate the effectiveness of online shielding techniques. Our experiments involve comparing the performance of RL agents with and without online shielding under various attack scenarios. We measure performance metrics such as task completion rate, reward accumulation, and generalization ability to assess the impact of online shielding on agent robustness and overall performance.

The results of our experiments demonstrate that online shielding techniques can significantly improve the robustness of RL agents against adversarial attacks. We observe that certain shielding methods effectively reduce the impact of adversarial perturbations, leading to more reliable and secure agent behavior. However, we also find that some shielding techniques may introduce performance trade-offs, such as reduced learning efficiency or increased computational overhead.

Based on our findings, we provide insights into the strengths and limitations of different online shielding approaches and discuss potential directions for future research. This study contributes to a better understanding of the impact of online shielding on reinforcement learning and provides practical guidance for developing secure and robust RL agents in adversarial environments.

Keywords: Reinforcement learning, online shielding, adversarial attacks, defense mechanisms, robustness, performance evaluation.

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