Counterfactually Safe Reinforcement Learning

Jingyi Li; Peng Wu; Chengchun Shi

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arXiv · 2605.25114

Counterfactually Safe Reinforcement Learning

Jingyi Li·Peng Wu·Chengchun Shi

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Abstract

Reinforcement learning algorithms are generally designed to maximize the expected return across a population. However, a policy that is optimal on average may be suboptimal for certain individuals, leading to potential safety concerns. To address this, we first formalize the notion of individual harm from a counterfactual perspective and define harm as the event in which a chosen action results in a strictly worse outcome than a baseline alternative. We then propose a general two-stage procedure for learning policies that maximize the expected return while accounting for individual harm. We further establish the finite-sample properties of the learned policy, derive an upper bound on its sub-optimality gap, and show that the harm rate remains well-controlled. Numerical experiments on both simulated and real-world datasets demonstrate the effectiveness of the proposed approach.

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BibTeX RIS

Jingyi Li, Peng Wu, Chengchun Shi. 2026-05-24. Counterfactually Safe Reinforcement Learning. https://arxiv.org/abs/2605.25114

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