Algorithms for Deciding the Safety of States in Fully Observable Non-deterministic Problems: Technical Report
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918391371005952 |
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| author | Schmalz, Johannes Jain, Chaahat |
| author_facet | Schmalz, Johannes Jain, Chaahat |
| contents | Learned action policies are increasingly popular in sequential decision-making, but suffer from a lack of safety guarantees. Recent work introduced a pipeline for testing the safety of such policies under initial-state and action-outcome non-determinism. At the pipeline's core, is the problem of deciding whether a state is safe (a safe policy exists from the state) and finding faults, which are state-action pairs that transition from a safe state to an unsafe one. Their most effective algorithm for deciding safety, TarjanSafe, is effective on their benchmarks, but we show that it has exponential worst-case runtime with respect to the state space. A linear-time alternative exists, but it is slower in practice. We close this gap with a new policy-iteration algorithm iPI, that combines the best of both: it matches TarjanSafe's best-case runtime while guaranteeing a polynomial worst-case. Experiments confirm our theory and show that in problems amenable to TarjanSafe iPI has similar performance, whereas in ill-suited problems iPI scales exponentially better. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_15282 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Algorithms for Deciding the Safety of States in Fully Observable Non-deterministic Problems: Technical Report Schmalz, Johannes Jain, Chaahat Artificial Intelligence Learned action policies are increasingly popular in sequential decision-making, but suffer from a lack of safety guarantees. Recent work introduced a pipeline for testing the safety of such policies under initial-state and action-outcome non-determinism. At the pipeline's core, is the problem of deciding whether a state is safe (a safe policy exists from the state) and finding faults, which are state-action pairs that transition from a safe state to an unsafe one. Their most effective algorithm for deciding safety, TarjanSafe, is effective on their benchmarks, but we show that it has exponential worst-case runtime with respect to the state space. A linear-time alternative exists, but it is slower in practice. We close this gap with a new policy-iteration algorithm iPI, that combines the best of both: it matches TarjanSafe's best-case runtime while guaranteeing a polynomial worst-case. Experiments confirm our theory and show that in problems amenable to TarjanSafe iPI has similar performance, whereas in ill-suited problems iPI scales exponentially better. |
| title | Algorithms for Deciding the Safety of States in Fully Observable Non-deterministic Problems: Technical Report |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.15282 |