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
The increasing deployment of AI in safety-critical applications necessitates robust methods for safe learning, driving accelerated research into offline reinforcement learning techniques.
Improving the safety and reliability of AI systems is crucial for their broader adoption, particularly in real-world scenarios where failures can have severe consequences.
This research introduces a more reliable method for learning safe policies from static datasets, potentially speeding up the development and deployment of autonomous systems with strict safety requirements.
- · AI developers
- · Robotics companies
- · Autonomous vehicle manufacturers
- · Industrial automation
- · Companies relying on less rigorous safety-constrained AI
- · Manual control systems in hazardous environments
Enhances the feasibility of deploying AI in highly regulated and safety-critical sectors.
Could accelerate the development of advanced AI agents capable of operating with greater autonomy and less human oversight.
May contribute to the broader availability of safe and reliable AI systems, impacting industries from healthcare to defense.
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Read at arXiv cs.LG