GymPN: A Library for Decision-Making in Process Management Systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913912254889984 |
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| author | Bianco, Riccardo Lo van Jaarsveld, Willem Dijkman, Remco |
| author_facet | Bianco, Riccardo Lo van Jaarsveld, Willem Dijkman, Remco |
| contents | Process management systems support key decisions about the way work is allocated in organizations. This includes decisions on which task to perform next, when to execute the task, and who to assign the task to. Suitable software tools are required to support these decisions in a way that is optimal for the organization. This paper presents a software library, called GymPN, that supports optimal decision-making in business processes using Deep Reinforcement Learning. GymPN builds on previous work that supports task assignment in business processes, introducing two key novelties: support for partial process observability and the ability to model multiple decisions in a business process. These novel elements address fundamental limitations of previous work and thus enable the representation of more realistic process decisions. We evaluate the library on eight typical business process decision-making problem patterns, showing that GymPN allows for easy modeling of the desired problems, as well as learning optimal decision policies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GymPN: A Library for Decision-Making in Process Management Systems Bianco, Riccardo Lo van Jaarsveld, Willem Dijkman, Remco Artificial Intelligence Process management systems support key decisions about the way work is allocated in organizations. This includes decisions on which task to perform next, when to execute the task, and who to assign the task to. Suitable software tools are required to support these decisions in a way that is optimal for the organization. This paper presents a software library, called GymPN, that supports optimal decision-making in business processes using Deep Reinforcement Learning. GymPN builds on previous work that supports task assignment in business processes, introducing two key novelties: support for partial process observability and the ability to model multiple decisions in a business process. These novel elements address fundamental limitations of previous work and thus enable the representation of more realistic process decisions. We evaluate the library on eight typical business process decision-making problem patterns, showing that GymPN allows for easy modeling of the desired problems, as well as learning optimal decision policies. |
| title | GymPN: A Library for Decision-Making in Process Management Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20404 |