petrirl 0.2.1

PetriRL : Advanced Scheduling with Petri Nets and Reinforcement Learning

PetriRL: Explainable Reinforcement Learning with Petri Nets

PetriRL is a research framework that integrates Colored-Timed Petri Nets (CTPNs) with Reinforcement Learning (RL) to solve complex scheduling and optimization problems in Flexible Manufacturing Systems (FMS) and beyond.

Developed as part of my PhD on Explainable Model-Based Reinforcement Learning for Discrete Event Systems, PetriRL provides a formal, modular, and reproducible approach to dynamic scheduling, intralogistics, and decision-making under uncertainty.

Core Idea

PetriRL bridges Petri net modeling and RL-based decision-making:

• Petri Nets provide formal semantics, modular scalability, and explainability via token flow and graphical structure.
• Reinforcement Learning provides adaptability, generalization, and efficient online decision-making under dynamic conditions.
• Dynamic Action Masking (from Petri net guard functions) reduces the RL action space, improving sample efficiency and convergence.

This combination enables interpretable, scalable, and high-performing RL agents for scheduling, dispatching, and optimization in discrete event systems.

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PetriRL Environments

The PetriRL framework is organized into multiple environments, each targeting a different class of problems:

• 🔹 PetriRL Intralogistics
  Dynamic scheduling of AGVs, tool sharing, and shop floor optimization in Flexible Manufacturing Systems.

• 🔹 PetriRL DJJSP
  Dynamic Job Shop Scheduling Problem (DJJSP) with machine breakdowns, variable job arrivals, and stochastic environments.

• 🔹 PetriRL Hyperheuristics (Planned for 2025)
  A super-heuristic framework where RL learns to combine and select dispatching rules for adaptive scheduling.

• 🔹 PetriRL Multi-Objective Optimization (Planned 2026)
  Combines expert agents and actor-critic RL to solve scheduling problems with multiple objectives (e.g., makespan, tardiness, energy consumption).

Each environment is OpenAI Gym-compatible, comes with benchmarks (Taillard + custom large-scale instances), and is designed for reproducibility and extension.

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Why PetriRL?

✔ Explainable: Token flows provide a clear semantic interpretation of RL decisions.
✔ Scalable: Modular Petri net design supports complex and large-scale systems.
✔ Efficient: Action masking reduces search space and improves training efficiency.
✔ Adaptive: RL agents generalize to unseen problems and adapt to disruptions in real time.
✔ Research-Ready: Benchmarks, reproducible pipelines, and Gym compatibility.

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Installation

Install the core PetriRL package via pip:

pip install petrirl

Specific environments can be created by selecting the corresponding Gym environment ID:

1. Logistics:

 env = gym.make("petrirl-djssp-v0")

2. Fault tolerant:

 env = gym.make("petrirl-dft-v0")

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Research Context

PetriRL has been applied to:

• Flexible Manufacturing Systems intralogistics (AGV + tool sharing).
• Dynamic job shop scheduling with stochasticity.
• RL-based hyperheuristics for dispatching rule selection.
• Multi-objective optimization in scheduling.

These works are part of my PhD and published in international journals:

• Flexible Manufacturing Systems intralogistics: Dynamic optimization of AGVs and tool sharing using Colored-Timed Petri Nets and actor–critic RL with actions masking
  ScienceDirect Link

• Introducing PetriRL: An innovative framework for JSSP resolution integrating Petri nets and event-based reinforcement learning
  ScienceDirect Link

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Citation

If you use this framework, please cite the associated papers:

@article{
title = {Introducing PetriRL: An innovative framework for JSSP resolution integrating Petri nets and event-based reinforcement learning},
author = {Sofiene Lassoued and Andreas Schwung},
journal = {Journal of Manufacturing Systems},
volume = {74},
pages = {690-702},
year = {2024},
issn = {0278-6125},
doi = {https://doi.org/10.1016/j.jmsy.2024.04.028},
url = {https://www.sciencedirect.com/science/article/pii/S0278612524000943},
}

@article{
title = {Flexible Manufacturing Systems intralogistics: Dynamic optimization of AGVs and tool sharing using Colored-Timed Petri Nets and actor–critic RL with actions masking},
author = {Sofiene Lassoued, Laxmikant Shrikant Baheti, Nathalie Weiß-Borkowski, Stefan Lier, Andreas Schwung}
journal = {Journal of Manufacturing Systems},
volume = {82},
pages = {405-419},
year = {2025},
issn = {0278-6125},
doi = {https://doi.org/10.1016/j.jmsy.2025.06.017.},
url = {https://www.sciencedirect.com/science/article/pii/S0278612525001694},

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Contributions

This repository provides the main entry point to PetriRL, including:

• The core PetriRL package (pip install petrirl).
• Links to specialized environments.
• Documentation and research context.
• Benchmarks and reproducible pipelines for evaluation.