arXiv Machine Learning
Safe Meta-Reinforcement Learning via Information Space Reachability
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SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
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Evaluation Metrics for Safe Reinforcement Learning
The paper introduces new evaluation metrics for safe reinforcement learning that go beyond average safety guarantees by examining how often and how severely safety bounds are violated, consistency across tasks and bounds, and the relationship between training-time and final policy behavior. It also proposes a safety tier system for categorizing algorithms and presents empirical safety evaluations on multiple navigation tasks. The authors recommend reporting aggregate metrics, distributional data, and task‑specific results together, and provide an open‑source suite, SafeRLEval, to facilitate reliable safety assessment.
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Safe Exploration via Policy Priors
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Safe In-Context Reinforcement Learning
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Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
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