Safe Reinforcement Learning with Preference-based Constraint Inference
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 alternative, we identify popular Bradley-Terry (BT) models fail to capture the asymmetric, heavy-tailed nature of safety costs, resulting in risk underestimati...
| Authors | Chenglin Li, Grant Ruan, Hua Geng |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 22 May 2026 |
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