Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems
This addresses process optimization in decentralized manufacturing systems, offering incremental improvements through knowledge sharing among similar players.
The paper tackles distributed self-optimization in manufacturing systems by introducing an online transfer learning approach for state-based potential games, which improves production efficiency and reduces power consumption compared to baseline methods.
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.