---
publication_id: "OT-2026-000001650"
title: "Multi-agent reinforcement learning framework for dynamic dispatching in material handling systems"
published_at: "2026-09-15T19:07:20.411639Z"
canonical_url: "https://opentechnical.org/p/OT-2026-000001650"
categories:
  - "material-handling/conveying-systems"
keywords:
  - "b65g1/1373"
  - "b65g1/1373"
  - "g05d1/667"
  - "b65g1/137"
  - "g05d101/15"
  - "g05d105/20"
---

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

## 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.