Implementing reinforcement learning in simio discrete-event simulation software

Andrew Greasley*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


A significant barrier to the combined use of simulation and AI techniques such as Machine Learning (ML) are that developers have differing backgrounds and use different tools. In particular simulation model developers may lack expertise in the software tools and coding abilities needed for the development of ML algorithms. In order to bridge this gap this article presents a discrete-event simulation (DES) that incorporates the use of a reinforcement learning (RL) algorithm which determines an approximate best route for robots in a factory moving from one physical location to another whilst avoiding collisions with fixed barriers. The study shows how the object modelling and graphical facilities of the Simio commercial off-the-shelf (COTS) DES software package enables an RL capability without the need to use program code or require an interface with external RL software.

Original languageEnglish
Article number27
Pages (from-to)314-324
Number of pages11
JournalSimulation Series
Issue number3
Publication statusPublished - 22 Jul 2020
Event2020 Summer Computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020 - Virtual, Online
Duration: 20 Jul 202022 Jul 2020


  • Autonomous robot
  • Machine learning
  • Reinforcement learning


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