Safe Reinforcement Learning with Preference-based Constraint Inference

arXiv:2603.23565v2 Announce Type: replace Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restrictive assumptions or extensive expert demonstrations, which are not realistic in many real-world applications. How to cheaply and reliably learn these constraints is the major challenge we focus on in this study. While inferring constraints from human preferences offers a data-efficient alternativ

Source: arXiv cs.LG — read the full report at the original publisher.

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