Counterfactually Safe Reinforcement Learning

• URL: http://arxiv.org/abs/2605.25114v1
• Date: Sun, 24 May 2026 14:54:06 GMT
• Title: Counterfactually Safe Reinforcement Learning
• Abstract summary: Reinforcement learning algorithms are generally designed to maximize the expected return across a population.<n>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.<n>We then propose a general two-stage procedure for learning policies that maximize the expected return while accounting for individual harm.
• Score: 25.8534002955263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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