Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective

• URL: http://arxiv.org/abs/2602.23816v1
• Date: Fri, 27 Feb 2026 08:55:29 GMT
• Title: Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective
• Authors: George Papadopoulos, George A. Vouros,
• Abstract summary: We find a policy that maximizes the likelihood of demonstrated trajectories trading the balance between being conservative and increasing significantly the likelihood of high-rewarding trajectories.<n>We formulate the promise" of individual state-action pairs in terms of $Q$ values, which depend on task-specific rewards as well as on the assessment of states' safety.<n>This entails a safe Q-learning perspective of the inverse learning problem under constraints.
• Score: 1.104960878651584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a set of trajectories demonstrating the execution of a task safely in a constrained MDP with observable rewards but with unknown constraints and non-observable costs, we aim to find a policy that maximizes the likelihood of demonstrated trajectories trading the balance between being conservative and increasing significantly the likelihood of high-rewarding trajectories but with potentially unsafe steps. Having these objectives, we aim towards learning a policy that maximizes the probability of the most $promising$ trajectories with respect to the demonstrations. In so doing, we formulate the ``promise" of individual state-action pairs in terms of $Q$ values, which depend on task-specific rewards as well as on the assessment of states' safety, mixing expectations in terms of rewards and safety. This entails a safe Q-learning perspective of the inverse learning problem under constraints: The devised Safe $Q$ Inverse Constrained Reinforcement Learning (SafeQIL) algorithm is compared to state-of-the art inverse constraint reinforcement learning algorithms to a set of challenging benchmark tasks, showing its merits.

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