On Corruption-Robustness in Performative Reinforcement Learning

• URL: http://arxiv.org/abs/2505.05609v1
• Date: Thu, 08 May 2025 19:37:35 GMT
• Title: On Corruption-Robustness in Performative Reinforcement Learning
• Authors: Vasilis Pollatos, Debmalya Mandal, Goran Radanovic,
• Abstract summary: We study the convergence of repeated retraining approaches to a performatively stable policy.<n>We extend these approaches to operate under corrupted data.<n>We prove that our approach exhibits last-ite convergence to an approximately stable policy.
• Score: 13.509499718691016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In performative Reinforcement Learning (RL), an agent faces a policy-dependent environment: the reward and transition functions depend on the agent's policy. Prior work on performative RL has studied the convergence of repeated retraining approaches to a performatively stable policy. In the finite sample regime, these approaches repeatedly solve for a saddle point of a convex-concave objective, which estimates the Lagrangian of a regularized version of the reinforcement learning problem. In this paper, we aim to extend such repeated retraining approaches, enabling them to operate under corrupted data. More specifically, we consider Huber's $\epsilon$-contamination model, where an $\epsilon$ fraction of data points is corrupted by arbitrary adversarial noise. We propose a repeated retraining approach based on convex-concave optimization under corrupted gradients and a novel problem-specific robust mean estimator for the gradients. We prove that our approach exhibits last-iterate convergence to an approximately stable policy, with the approximation error linear in $\sqrt{\epsilon}$. We experimentally demonstrate the importance of accounting for corruption in performative RL.

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