mlsanityData to 14 Oct 2026

Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models

arXiv 2609.11615 ↗First posted 10 Sep 2026cs.AI cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 32 references that are in the map. First posted on arXiv 10 Sep 2026.

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References · 16

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4 more
  1. 2018Multi-Agent Reinforcement Learning: A Report on Challenges and Approachescited by 6
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  4. 2024Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systemscited by 1

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  3. 2026Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
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  5. 2026Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication
  6. 2025A Survey of Reinforcement Learning for Optimization in Automation
  7. 2025Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems
  8. 2025Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control