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papersTODAY 04:00 UTC

arXiv paper surveys evaluation metrics for safe reinforcement learning

A new arXiv preprint examines how researchers measure performance in safe reinforcement learning, where an agent must maximize reward while keeping cumulative cost under a defined limit. The authors argue that existing benchmarks and metrics do not fully capture safety performance, and propose a framework for comparing methods more consistently. The work is an announcement-only cross-listing and has not been peer reviewed.

arXivai-safetybenchmarkingpeer reviewreinforcement-learningsafe reinforcement learning

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIEvaluation Metrics for Safe Reinforcement Learning ↗TODAY 04:00 UTC
arXiv cs.LGEvaluation Metrics for Safe Reinforcement Learning ↗TODAY 04:00 UTC

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