GymPN: A Library for Decision-Making in Process Management Systems
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 25 Jun 2025.
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References · 5
- 2020OR-Gym: A Reinforcement Learning Library for Operations Research Problemscited by 51
- 2017Simulation optimization: A review of algorithms and applicationscited by 23
- 2024Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learningcited by 6
- 2023Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problemscited by 4
- 2025Automated decision-making for dynamic task assignment at scalecited by 1
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- 2025A Universal Approach to Feature Representation in Dynamic Task Assignment Problems
- 2025RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes
- 2024Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
- 2026AI-Decision Checkpoints for AI-Augmented Business Process Management: Framework and Educational Instantiation
- 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering
- 2025Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective