Counterfactually Safe Reinforcement Learning
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 ...
| Authors | Jingyi Li, Peng Wu, Chengchun Shi |
|---|---|
| Primary category | stat.ML |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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