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Design of track path-finding simulation using Unity ML Agents

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(2), pp.61-66
  • DOI : 10.9708/jksci.2024.29.02.061
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 16, 2024
  • Accepted : February 13, 2024
  • Published : February 29, 2024

In-Chul Han 1 Jin-Woong Kim 1 Soo Kyun Kim 1

1제주대학교

Accredited

ABSTRACT

This paper aims to design a simulation for path-finding of objects in a simulation or game environment using reinforcement learning techniques. The main feature of this study is that the objects in the simulation are trained to avoid obstacles at random locations generated on a given track and to automatically explore path to get items. To implement the simulation, ML Agents provided by Unity Game Engine were used, and a learning policy based on PPO (Proximal Policy Optimization) was established to form a reinforcement learning environment. Through the reinforcement learning-based simulation designed in this study, we were able to confirm that the object moves on the track by avoiding obstacles and exploring path to acquire items as it learns, by analyzing the simulation results and learning result graph.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.