Evaluation Metrics for Safe Reinforcement Learning
Safe reinforcement learning (RL) is commonly formalized as a Constrained Markov Decision Process (CMDP), in which an agent maximizes expected reward while keeping its expected cumulative cost below a specified safety bound. Existing safe RL benchmarks predominantly report whether an algorithm is safe on average, following this expectation-based guarantee. We argue that this convention is insufficient to reliably characterize an algorithm's true safety: it fails to capture how often and how severely the safety bound is violated, whether this holds consistently across tasks and safety bounds, and whether training-time behavior is representative of behavior of the final converged policy. The...
| Authors | Lindsay Spoor, Aske Plaat, Thomas Moerland |
|---|---|
| Primary category | cs.AI |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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