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New Variations for Strategy Set-shifting in the Rat

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Offline Reward Perturbation Boosts Distributional Shift in Online RL.

Zishun Yu¹, Siteng Kang¹, Xinhua Zhang¹

¹Department of Computer Science, University of Illinois Chicago, Chicago, IL, USA.

Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence

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Summary

This study introduces a stealthy data poisoning attack targeting offline-to-online reinforcement learning (RL). The novel method exploits critic-regularized RL vulnerabilities, causing significant performance drops during online fine-tuning with minimal data.

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Area of Science:

Artificial Intelligence
Machine Learning
Reinforcement Learning

Background:

Offline-to-online reinforcement learning (RL) reduces online sample complexity by leveraging pre-collected offline data.
This approach introduces new vulnerabilities to data poisoning attacks targeting the offline training phase.

Purpose of the Study:

To reveal vulnerabilities in critic-regularized offline RL against novel data poisoning attacks.
To propose a stealthy attack method that maintains performance during offline training but degrades online fine-tuning.

Main Methods:

Leveraging bi-level optimization techniques.
Promoting over-estimation and distribution shift within the offline-to-online RL framework.
Developing a stealthy attack with a small budget and without white-box access.

Main Results:

Demonstrated stealthiness: offline training performance remains intact.
Significant performance degradation observed during the online fine-tuning stage.
Attack effectiveness confirmed across four experimental environments.

Conclusions:

Critic-regularized offline-to-online RL is vulnerable to stealthy data poisoning attacks.
The proposed bi-level optimization method effectively exploits these vulnerabilities.
The attack is practical, requiring minimal resources and no white-box access.