14 citations · 17 across the 12 of their papers we have counts for
Showing 2026 · cs.AIShow all
2 papers · 2 filters
cs.AI2026
Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
Jonathan Hoss, Noah Klarmann
Event-driven scheduling policies are increasingly deployed in industrial environments, where decisions are made under asynchronous and partially observed system states. As a result…
cs.AI2026
An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
Moritz Link, Jonathan Hoss, Noah Klarmann
Efficient job-shop scheduling with transportation resources is critical for high-performance manufacturing. With the rise of "decentralized factories", multi-agent reinforcement le…