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Reinforcement Learning in Anylogic Simulation Models: A Guiding Example Using Pathmind
Conference proceeding

Reinforcement Learning in Anylogic Simulation Models: A Guiding Example Using Pathmind

Mohammed Farhan, Brett Gohre, Edward Junprung and IEEE
2020 Winter Simulation Conference (WSC), Vol.2020-, pp.3212-3223
14/12/2020
DOI: https://doi.org/10.1109/WSC48552.2020.9383916

Abstract

Customer services Industries Reinforcement learning Software Throughput
Reinforcement Learning has recently gained a lot of exposure in the simulation industry. In this paper, we demonstrate the use of reinforcement learning in AnyLogic software models using Pathmind. A coffee shop simulation is built to train a barista to make correct operational decisions and improve efficiency that directly affects customer service time. The trained policy outperforms rule-based functions in terms of customer service time and throughput.

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