Evaluating the impact of curriculum learning on the training process for an intelligent agent in a video game
DOI:
https://doi.org/10.4114/intartif.vol24iss68pp1-20Keywords:
Curriculum Learning, Game AI, Unity Machine Learning Agents, Training Curriculum, Mean Cumulative Reward, Proximal Policy OptimizationAbstract
We want to measure the impact of the curriculum learning technique on a reinforcement training setup, several experiments were designed with different training curriculums adapted for the video game chosen as a case study. Then all were executed on a selected game simulation platform, using two reinforcement learning algorithms, and using the mean cumulative reward as a performance measure. Results suggest that curriculum learning has a significant impact on the training process, increasing training times in some cases, and decreasing them up to 40% percent in some other cases.
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Copyright (c) 2021 Iberamia & The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Open Access publishing.
Lic. under Creative Commons CC-BY-NC
Inteligencia Artificial (Ed. IBERAMIA)
ISSN: 1988-3064 (on line).
(C) IBERAMIA & The Authors