Data Poisoning Attacks Significantly Degrade the Reliability of Off-Policy Policy Evaluation Methods

Core Concepts

Existing off-policy policy evaluation methods are highly vulnerable to small adversarial perturbations in the training data, which can lead to large errors in the estimated policy values.

Abstract

The article investigates the sensitivity of various off-policy policy evaluation (OPE) methods to adversarial data poisoning attacks. The key insights are: The authors propose a novel data poisoning attack framework called DOPE that leverages influence functions to construct small perturbations to the training data that can significantly degrade the performance of OPE methods. Extensive experiments on healthcare and control domains show that many existing OPE methods, such as Bellman Residual Minimization (BRM), Weighted Importance Sampling (WIS), and Weighted Doubly Robust (WDR), are highly prone to large errors in policy value estimates when subject to DOPE attacks, even with small adversarial perturbations. In contrast, Consistent Per-Decision Importance Sampling (CPDIS) and Weighted Importance Sampling (WIS) are relatively more robust to the DOPE attacks. The findings question the reliability of policy values derived using existing OPE methods and motivate the need for developing OPE methods that are statistically robust to train-time data poisoning attacks.

Stats

"Corrupting only 3%–5% of the observed states achieves more than 340% and 100% error in the estimate of the value function of the optimal policy in the HIV and MountainCar domains, respectively." "Even when corrupting only 5% of the data points, the attacker need not perturb the state features significantly to achieve large errors in the value estimate."

Quotes

"Our experimental results demonstrate that many existing OPE methods are highly prone to generating value estimates with large errors when subject to data poisoning attacks, even for small adversarial perturbations." "These findings question the reliability of policy values derived using OPE methods and motivate the need for developing OPE methods that are statistically robust to train-time data poisoning attacks."

Key Insights Distilled From

Data Poisoning Attacks on Off-Policy Policy Evaluation Methods

by Elita Lobo,H... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04714.pdf

Deeper Inquiries

How can we develop OPE methods that are inherently robust to adversarial data poisoning attacks

To develop OPE methods that are inherently robust to adversarial data poisoning attacks, several strategies can be employed: Regularization Techniques: Incorporating regularization techniques such as L1 or L2 regularization can help prevent overfitting to the training data and make the model more robust to small perturbations in the data. Outlier Detection: Implementing outlier detection mechanisms can help identify and mitigate the impact of adversarial data points that may be introduced during the training phase. Ensemble Methods: Utilizing ensemble methods can improve the robustness of OPE models by combining multiple models and reducing the impact of individual models that may be influenced by adversarial data. Adversarial Training: Training OPE models with adversarial examples can help the model learn to be more resilient to such attacks by exposing it to a variety of perturbed data during the training process. Feature Engineering: Careful feature selection and engineering can help reduce the model's sensitivity to adversarial perturbations by focusing on relevant and robust features that are less likely to be manipulated. By incorporating these strategies and potentially exploring new techniques specifically designed to enhance the robustness of OPE methods to adversarial attacks, we can develop more reliable and secure OPE models.

What are the potential implications of unreliable OPE estimates in high-stakes decision-making domains like healthcare

The implications of unreliable OPE estimates in high-stakes decision-making domains like healthcare can be significant and far-reaching: Patient Safety: Inaccurate OPE estimates could lead to the deployment of suboptimal or potentially harmful policies in healthcare settings, jeopardizing patient safety and well-being. Resource Allocation: Incorrect policy evaluations can result in misallocation of resources, leading to inefficiencies in healthcare systems and potentially impacting patient care and outcomes. Ethical Concerns: Unreliable OPE estimates could raise ethical concerns, especially in healthcare, where decisions directly impact individuals' health and lives. Ensuring the accuracy and reliability of policy evaluations is crucial to maintaining ethical standards. Trust and Confidence: Stakeholders in the healthcare industry rely on OPE methods to make informed decisions. Unreliable estimates can erode trust in the decision-making process and the effectiveness of policies implemented based on those estimates. Legal Ramifications: In cases where decisions based on OPE estimates lead to adverse outcomes, there could be legal implications for the individuals or organizations involved. Ensuring the reliability of OPE methods is essential to mitigate legal risks. Overall, the implications of unreliable OPE estimates in healthcare underscore the critical need for robust and trustworthy evaluation methods in high-stakes decision-making domains.

Can the insights from this work be extended to other areas of reinforcement learning beyond OPE, such as policy learning and planning

The insights from this work on data poisoning attacks in OPE methods can be extended to other areas of reinforcement learning beyond OPE, such as policy learning and planning, in the following ways: Policy Learning: Understanding the vulnerabilities of OPE methods to adversarial attacks can inform the development of more robust policy learning algorithms. By incorporating defenses against data poisoning, policy learning models can be designed to withstand malicious manipulations of training data. Planning Algorithms: Similar to OPE methods, planning algorithms rely on accurate estimates of value functions to make decisions. Insights from this work can be applied to enhance the robustness of planning algorithms to adversarial data perturbations, ensuring more reliable decision-making in dynamic environments. Model Evaluation: The concepts of influence functions and robustness to data poisoning can be utilized in evaluating the performance and reliability of reinforcement learning models in general. By assessing the susceptibility of models to adversarial attacks, researchers can develop more secure and dependable algorithms across various RL applications. By extending the insights gained from studying data poisoning attacks in OPE methods to other areas of reinforcement learning, researchers can enhance the overall resilience and trustworthiness of RL algorithms in diverse domains.