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

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.

Authors 4

Andreas Schwung, Steve Yuwono, Sofiene Lassoued, Dorothea Schwung

Specification

Official page: arxiv.org/abs/2609.11615
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
arXiv id: 2609.11615
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Categories: cs.AI, cs.LG, cs.SY, eess.SY
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
PDF: arxiv.org/pdf/2609.11615
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Primary category: cs.AI
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

5 h ago

Conflicts

None

No models linked to this paper yet.

Authors: Andreas Schwung, Steve Yuwono, Sofiene Lassoued

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · Abstract

1 claims · 1 propertiesShow all properties

Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
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.	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperDistributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
New paper: Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
arxiv
rss.arxiv.org

Full timeline →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	3 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.