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This research advances model-based reinforcement learning by integrating approximate inverse models to disentangle actuation dynamics from state space dynamics, enabling efficient training in task space for modular manufacturing. The study demonstrates significant improvements in performance and training speed for off-policy algorithms within a heterogeneous production testbed, offering a scalable framework for data-driven control of flexible systems.
Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
arXiv:2609.11615v1 Announce Type: new Abstract: This paper presents a novel approach for data-driven self-learning control of highly flexible, modular manufacturing systems. Specifically, we employ a novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies. This approach disentangles the learning of actuation dynamics and the dynamics in state space, resulting in RL-based training solely within the task space. We propose a lightweight feedforward architecture for approximate inverse models and integrate them within the policy network of standard RL algorithms. We apply the approach to a laboratory modular production testbed with heterogeneous production modules. The results underline the efficiency improvements for modular manufacturing units in terms of both performance and training speed, particularly for off-policy algorithms.
Abstract:This paper presents a novel approach for data-driven self-learning control of highly flexible, modular manufacturing systems. Specifically, we employ a novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies. This approach disentangles the learning of actuation dynamics and the dynamics in state space, resulting in RL-based training solely within the task space. We propose a lightweight feedforward architecture for approximate inverse models and integrate them within the policy network of standard RL algorithms. We apply the approach to a laboratory modular production testbed with heterogeneous production modules. The results underline the efficiency improvements for modular manufacturing units in terms of both performance and training speed, particularly for off-policy algorithms.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
arXiv:2609.11615 [cs.AI]
(or arXiv:2609.11615v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.11615 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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