Learning robust policies for uncertain parametric Markov decision processes
Synthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust manner, while at the same time it is often of interest to avoid being overly conservative with the view of achieving a be...
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| Format: | Conference item |
| Language: | English |
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Journal of Machine Learning Research
2024
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| _version_ | 1826313744283598848 |
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| author | Rickard, L Abate, A Margellos, K |
| author_facet | Rickard, L Abate, A Margellos, K |
| author_sort | Rickard, L |
| collection | OXFORD |
| description | Synthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust manner, while at the same time it is often of interest to avoid being overly conservative with the view of achieving a better cost. We propose a method for verifiably safe policy synthesis for a class of finite state models, under the presence of structural uncertainty. In particular, we consider uncertain parametric Markov decision processes (upMDPs), a special class of Markov decision processes, with parameterised transition functions, where such parameters are drawn from a (potentially) unknown distribution. Our framework leverages recent advancements in the so-called scenario approach theory, where we represent the uncertainty by means of scenarios, and provide guarantees on synthesised policies satisfying probabilistic computation tree logic (PCTL) formulae. We consider several common benchmarks/problems and compare our work to recent developments for verifying upMDPs. |
| first_indexed | 2024-09-25T04:19:44Z |
| format | Conference item |
| id | oxford-uuid:f92e0cff-c024-4f20-85dc-95e83dfceb14 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:19:44Z |
| publishDate | 2024 |
| publisher | Journal of Machine Learning Research |
| record_format | dspace |
| spelling | oxford-uuid:f92e0cff-c024-4f20-85dc-95e83dfceb142024-08-01T12:22:45ZLearning robust policies for uncertain parametric Markov decision processesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:f92e0cff-c024-4f20-85dc-95e83dfceb14EnglishSymplectic ElementsJournal of Machine Learning Research2024Rickard, LAbate, AMargellos, KSynthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust manner, while at the same time it is often of interest to avoid being overly conservative with the view of achieving a better cost. We propose a method for verifiably safe policy synthesis for a class of finite state models, under the presence of structural uncertainty. In particular, we consider uncertain parametric Markov decision processes (upMDPs), a special class of Markov decision processes, with parameterised transition functions, where such parameters are drawn from a (potentially) unknown distribution. Our framework leverages recent advancements in the so-called scenario approach theory, where we represent the uncertainty by means of scenarios, and provide guarantees on synthesised policies satisfying probabilistic computation tree logic (PCTL) formulae. We consider several common benchmarks/problems and compare our work to recent developments for verifying upMDPs. |
| spellingShingle | Rickard, L Abate, A Margellos, K Learning robust policies for uncertain parametric Markov decision processes |
| title | Learning robust policies for uncertain parametric Markov decision processes |
| title_full | Learning robust policies for uncertain parametric Markov decision processes |
| title_fullStr | Learning robust policies for uncertain parametric Markov decision processes |
| title_full_unstemmed | Learning robust policies for uncertain parametric Markov decision processes |
| title_short | Learning robust policies for uncertain parametric Markov decision processes |
| title_sort | learning robust policies for uncertain parametric markov decision processes |
| work_keys_str_mv | AT rickardl learningrobustpoliciesforuncertainparametricmarkovdecisionprocesses AT abatea learningrobustpoliciesforuncertainparametricmarkovdecisionprocesses AT margellosk learningrobustpoliciesforuncertainparametricmarkovdecisionprocesses |