arXiv AI
EpiFlow: Bridging Safety and Performance in Offline Reinforcement Learning via Epigraph-Guided Flow Matching
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arXiv AI
Jul 10
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
arXiv:2603. 15136v2 Announce Type: replace-cross Abstract: Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints.
arXiv AI
Jun 15
CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning
arXiv:2606. 14415v1 Announce Type: new Abstract: Safe reinforcement learning (Safe RL) aims to maximize expected return while satisfying safety constraints, typically modeled as Constrained Markov Decision Processes (CMDPs).
arXiv Machine Learning
Jun 9
Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning
arXiv:2605. 26452v2 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints during both training and deployment.
arXiv AI
Sep 15
ShieldVLA: Feasibility-Aware Safety Alignment for Vision-Language-Action Models
arXiv:2609.13231v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existing fine-tuning methods provid...
arXiv Machine Learning
Sep 3
Exchange Policy Optimization Algorithm for Semi-Infinite Safe Reinforcement Learning
The paper introduces Exchange Policy Optimization (EPO), a framework for semi‑infinite safe reinforcement learning that handles infinitely many constraints by iteratively solving finite subproblems. EPO expands or deletes constraints based on tolerance violations and Lagrange multipliers, maintaining computational tractability while converging to an optimal policy with bounded safety violations. The authors prove finite convergence, provide iteration bounds, and quantify the suboptimality gap under mild assumptions.
arXiv Machine Learning
Aug 12
Boundary-Seeking Policy Gradient for Safe Reinforcement Learning
arXiv:2608. 10204v1 Announce Type: new Abstract: Safe reinforcement learning maximizes reward subject to safety constraints.