A simulation of autonomous robot movement directed by reinforcement learning

  • Andrew Greasley 
  • Aston University, Aston St, Birmingham B4 7ET, United Kingdom
Cite as
Greasley A. (2020). A simulation of autonomous robot movement directed by reinforcement learning. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 10-15. DOI: https://doi.org/10.46354/i3m.2020.emss.002


As companies embrace Industry 4.0 and embed intelligent robots and other intelligent facilities in their factories, decision making can be derived from machine learning algorithms and so if we are to simulate these systems we need to model these algorithms too. 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 oriented and graphical facilities of an industry ready 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. Thus the article aims to contribute to the methodology of simulation practitioners who wish to implement AI techniques as a supplement to their input modelling approaches.


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