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.
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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.