ReGuard discovers network scenarios where RL controllers perform 43-64% worse than achievable and reduces those gaps by 79-85% with lightweight rule-based protection that preserves normal performance.
Testing of deep rein- forcement learning agents with surrogate models.ACM Transactions on Software Engineering and Methodology, 33(3):73:1–73:33
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes Prior Random Testing (PRT) that leverages task difficulty to prioritize failure-prone test cases for DRL agents, achieving over 50% lower testing cost than random testing while preserving diversity on four benchmarks.
citing papers explorer
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Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers
ReGuard discovers network scenarios where RL controllers perform 43-64% worse than achievable and reduces those gaps by 79-85% with lightweight rule-based protection that preserves normal performance.
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Failure-Based Testing for Deep Reinforcement Learning Agents
Proposes Prior Random Testing (PRT) that leverages task difficulty to prioritize failure-prone test cases for DRL agents, achieving over 50% lower testing cost than random testing while preserving diversity on four benchmarks.