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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.07537 |
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| _version_ | 1866914543496593408 |
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| author | Brice, Léonard Cano, Filip Chatterjee, Krishnendu Henzinger, Thomas A. Muroya, Stefanie |
| author_facet | Brice, Léonard Cano, Filip Chatterjee, Krishnendu Henzinger, Thomas A. Muroya, Stefanie |
| contents | Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the current state. In a multi-environment POMDP (MEPOMDP), the initial state is unknown, and assumed to be adversarially chosen. In this work we focus on computing the optimal value and policy in MEPOMDPs with finite-horizon objectives. That problem is known to be PSPACE-complete in POMDPs. Our main results are as follows: (1) we establish that it is also PSPACE-complete in the more general setting of MEPOMDPs; (2) we present a practical algorithm and evaluate it on classical benchmarks, significantly outperforming the only previously known algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07537 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Environment POMDPs with Finite-Horizon Objectives Brice, Léonard Cano, Filip Chatterjee, Krishnendu Henzinger, Thomas A. Muroya, Stefanie Artificial Intelligence Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the current state. In a multi-environment POMDP (MEPOMDP), the initial state is unknown, and assumed to be adversarially chosen. In this work we focus on computing the optimal value and policy in MEPOMDPs with finite-horizon objectives. That problem is known to be PSPACE-complete in POMDPs. Our main results are as follows: (1) we establish that it is also PSPACE-complete in the more general setting of MEPOMDPs; (2) we present a practical algorithm and evaluate it on classical benchmarks, significantly outperforming the only previously known algorithm. |
| title | Multi-Environment POMDPs with Finite-Horizon Objectives |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.07537 |