# Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems

arXiv: https://arxiv.org/abs/2406.10015 · HTML: https://mlsanity.com/paper/2406.10015/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems](https://mlsanity.com/paper/1906.00331/) (2019)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval](https://mlsanity.com/paper/1803.07726/) (2018)
- [Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods](https://mlsanity.com/paper/2202.07262/) (2022)
- [Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach](https://mlsanity.com/paper/2005.11064/) (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)

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

- [Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games](https://mlsanity.com/paper/2410.22912/) (2024)
- [Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems](https://mlsanity.com/paper/2408.06397/) (2024)
- [Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory](https://mlsanity.com/paper/2506.13164/) (2025)

## Abstract (arXiv)

In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexible distributed multi-agent production system. The incorporation of gradient-based learning in SbPGs reduces training times and achieves more optimal policies than its baseline.