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http://dx.doi.org/10.7583/JKGS.2019.19.6.61

A Study about the Usefulness of Reinforcement Learning in Business Simulation Games using PPO Algorithm  

Liang, Yi-Hong (School of Games, Hongik University)
Kang, Sin-Jin (School of Games, Hongik University)
Cho, Sung Hyun (School of Games, Hongik University)
Publication Information
Journal of Korea Game Society / v.19, no.6, 2019 , pp. 61-70 More about this Journal
Abstract
In this paper, we apply reinforcement learning in the field of management simulation game to check whether game agents achieve autonomously given goal. In this system, we apply PPO (Proximal Policy Optimization) algorithm in the Unity Machine Learning (ML) Agent environment and the game agent is designed to automatically find a way to play. Five game scenario simulation experiments were conducted to verify their usefulness. As a result, it was confirmed that the game agent achieves the goal through learning despite the change of environment variables in the game.
Keywords
Reinforcement Learning; Proximal Policy Optimization Algorithm; Game Agent;
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Times Cited By KSCI : 4  (Citation Analysis)
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