# Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems

arXiv: https://arxiv.org/abs/2408.05992 · HTML: https://mlsanity.com/paper/2408.05992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [What is being transferred in transfer learning?](https://mlsanity.com/paper/2008.11687/) (2020)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis](https://mlsanity.com/paper/2109.14595/) (2021)
- [Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study](https://mlsanity.com/paper/1912.12528/) (2019)
- [Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study](https://mlsanity.com/paper/1812.06325/) (2018)
- [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)
- [Self Optimisation and Automatic Code Generation by Evolutionary Algorithms in PLC based Controlling Processes](https://mlsanity.com/paper/2304.05638/) (2023)

## Abstract (arXiv)

This paper presents a novel online transfer learning approach in state-based potential games (TL-SbPGs) for distributed self-optimization in manufacturing systems. The approach targets practical industrial scenarios where knowledge sharing among similar players enhances learning in large-scale and decentralized environments. TL-SbPGs enable players to reuse learned policies from others, which improves learning outcomes and accelerates convergence. To accomplish this goal, we develop transfer learning concepts and similarity criteria for players, which offer two distinct settings: (a) predefined similarities between players and (b) dynamically inferred similarities between players during training. The applicability of the SbPG framework to transfer learning is formally established. Furthermore, we present a method to optimize the timing and weighting of knowledge transfer. Experimental results from a laboratory-scale testbed show that TL-SbPGs improve production efficiency and reduce power consumption compared to vanilla SbPGs.