Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents
This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functionally-grounded metrics like faithfulness and compactness, and on human-grounded proxies like subjective ratings or prediction accuracy. We suggest evaluating XRL methods by how effectively their generated explanations help to diagnose and fix malfunctioning reinforcement learning (RL) agents. We propose EvalXRL, a benchmark in which a Large Language Model (LLM) coding agent uses different XRL methods to diagnose a held-out malfunction in an RL agent, and then repair it. Our proposed benchmark iterates across (environment $\times$ malfuncti...
| Authors | Ram Rachum, Yotam Amitai, Bálint Gyevnár et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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