Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies

arXiv:2603.15136v2 Announce Type: replace Abstract: Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints. Existing methods often rely on soft expected-cost objectives or iterative generative inference, which can be insufficient for safety-critical real-time control. We propose Safe Flow Q-Learning (SafeFQL), which extends FQL to safe offline RL by combining a Hamilton--Jacobi reachability-inspired safety value function with an efficient one-step flow policy. SafeFQL learns the safety value via a self-consistency Bellman

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

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