Okada, H. Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task — Part3: Differential Evolution. Preprints2024, 2024020145. https://doi.org/10.20944/preprints202402.0145.v1
APA Style
Okada, H. (2024). Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task — Part3: Differential Evolution. Preprints. https://doi.org/10.20944/preprints202402.0145.v1
Chicago/Turabian Style
Okada, H. 2024 "Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task — Part3: Differential Evolution" Preprints. https://doi.org/10.20944/preprints202402.0145.v1
Abstract
Evolutionary algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1 and Part2, the author previously reported experimental evaluations on Genetic Algorithm and Evolution Strategy for reinforcement learning of neural networks, utilizing the Acrobot control task. This article constitutes Part3 of the series of comparative research. In this study, Differential Evolution is adopted as the third instance of major evolutionary algorithms. The experimental results show a statistically significant superiority of DE over both GA and ES (p < .01). In addition, DE exhibits its robustness to variations in hyperparameter configurations (the number of offsprings and generations). In the previous experiments, both ES and GA showed significant performance differences depending on the configurations, whereas in the experiment reported in this article, such differences are not detected for DE.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
