# Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games

arXiv: https://arxiv.org/abs/2410.22912 · HTML: https://mlsanity.com/paper/2410.22912/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms](https://mlsanity.com/paper/2109.12286/) (2021)
- [Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning](https://mlsanity.com/paper/2009.03836/) (2020)
- [Dynamic Potential Games in Communications: Fundamentals and Applications](https://mlsanity.com/paper/1509.01313/) (2015)
- [A Comprehensive Survey of Potential Game Approaches to Wireless Networks](https://mlsanity.com/paper/1506.07942/) (2015)
- [Stackelberg Game Approaches for Anti-jamming Defence in Wireless Networks](https://mlsanity.com/paper/1805.12308/) (2018)
- [Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems](https://mlsanity.com/paper/2406.10015/) (2024)
- [Self Optimisation and Automatic Code Generation by Evolutionary Algorithms in PLC based Controlling Processes](https://mlsanity.com/paper/2304.05638/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Collaborative Process Parameter Recommender System for Fleets of Networked Manufacturing Machines -- with Application to 3D Printing](https://mlsanity.com/paper/2506.12252/) (2025)
- [Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory](https://mlsanity.com/paper/2506.13164/) (2025)
- [Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models](https://mlsanity.com/paper/2609.11615/) (2026)

## Abstract (arXiv)

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders' decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial-parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5-13% while satisfying the production demand, which significantly improves potential (global objective) values.