Okada, H. Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task — Part4: Particle Swarm Optimization. Preprints2024, 2024030109. https://doi.org/10.20944/preprints202403.0109.v1
APA Style
Okada, H. (2024). Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task — Part4: Particle Swarm Optimization. Preprints. https://doi.org/10.20944/preprints202403.0109.v1
Chicago/Turabian Style
Okada, H. 2024 "Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task — Part4: Particle Swarm Optimization" Preprints. https://doi.org/10.20944/preprints202403.0109.v1
Abstract
Evolutionary algorithms and swarm intelligence 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 these algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1, 2 and 3, the author previously reported experimental evaluations on Evolution Strategy, Genetic Algorithm, and Differential Evolution for reinforcement learning of neural networks, utilizing the Acrobot control task. This article constitutes Part4 of the series of comparative research. In this study, Particle Swarm Optimization is adopted as an instance of major swarm intelligence algorithms. The experimental result shows that PSO performed worse than all of DE, GA and ES. The difference between PSO and DE was statistically significant (p<.01). In addition, PSO exhibited lower capability in exploring solutions in high-dimensional search spaces than DE, GA, and ES did. A larger swarm size compensated for the weakness of PSO in global exploration, thus making itself more beneficial than a larger number of swarm search iterations.
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.
