Learning from the Test: Self-Referential Differential Testing for Deep RL Agents
Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in real-world applications, ensuring their quality and reliability is paramount. Current works primarily focus on detecting safety-critical failures, often neglecting policy optimality, which can lead to reduced efficiency, user distrust, and economic losses. This oversight, compounded by the inherent "testing oracle problem" for optimality, leaves a significant gap in comprehensively evaluating DRL systems. To address this gap, we propose Delta (Differential Testing for DRL Agents), a novel and comprehensive framework that automatically identifi...
| Authors | Junda He, Jieke Shi, Zhou Yang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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