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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-2026077949-A1, on 19 March 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000001650

Multi-agent reinforcement learning framework for dynamic dispatching in material handling systems

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Classification
Material Handling › Conveying Systems
Inventors
GUPTA CHETAN, KATSUMATA DAISUKE, LEE XIAN YEOW, MATSUI TAKAHARU, WANG HAIYAN
Assignee
HITACHI LTD
Original source
US-2026077949-A1, the United States Patent and Trademark Office, 19 March 2026
Document SHA-256
d83afc39b9e79997a4bcf895b3be33ddae44865cb153dc3c4bfaab922e5e810b

Abstract

Systems and methods for implementation of a multi-agent reinforcement learning based decision system for a materials handling system, including initializing a simulation environment comprising decision points for dispatching materials and attributes of the materials handling system, the simulation environment configured to request a decision for materials dispatch at the decision points to the multi-agent reinforcement learning based decision system; initializing the reinforcement learning agents representative of the decision points for the multi-agent reinforcement learning based decision system; initializing domain expert heuristics for the decision points for the multi-agent reinforcement learning based decision system; and iteratively training the reinforcement learning based decision system with the initialized domain expert heuristics on the simulation environment.

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OpenTechnical Publication OT-2026-000001650, “Multi-agent reinforcement learning framework for dynamic dispatching in material handling systems,” published September 15, 2026 at 19:07:20 UTC, https://opentechnical.org/p/OT-2026-000001650.

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