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http://dx.doi.org/10.7583/JKGS.2020.20.1.57

Build reinforcement learning AI process for cooperative play with users  

Jung, Won-Joe (Dept. of R&D, GoodgameStudio Corp)
Publication Information
Journal of Korea Game Society / v.20, no.1, 2020 , pp. 57-66 More about this Journal
Abstract
The goal is to implement AI using reinforcement learning, which replaces the less favored Supporter in MOBA games. ML_Agent implements game rules, environment, observation information, rewards, and punishment. The experiment was divided into P and C group. Experiments were conducted to compare the cumulative compensation values and the number of deaths to draw conclusions. In group C, the mean cumulative compensation value was 3.3 higher than that in group P, and the total mean number of deaths was 3.15 lower. performed cooperative play to minimize death and maximize rewards was confirmed.
Keywords
AI; Reinforcement learning; Machine learning; Unity3D;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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