Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatch

arXiv:2508.20784v2 Announce Type: replace Abstract: Bus bunching remains a challenge for urban transit due to stochastic traffic and passenger demand. Traditional solutions rely

arXiv:2605.27566v1 Announce Type: new Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

arXiv:2605.15204v1 Announce Type: new Abstract: Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not

arXiv:2603.20537v1 Announce Type: new Abstract: Industrial process control demands policies that are interpretable and auditable, requirements that black-box neural policies

arXiv:2604.14032v1 Announce Type: new Abstract: Reinforcement learning has shown promise for automating power-grid operation tasks such as topology control and congestion

arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit

arXiv:2603.10651v2 Announce Type: replace-cross Abstract: Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it)