Okada, H. Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task – Part1: Evolution Strategy. Preprints2023, 2023080081. https://doi.org/10.20944/preprints202308.0081.v1
APA Style
Okada, H. (2023). Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task – Part1: Evolution Strategy. Preprints. https://doi.org/10.20944/preprints202308.0081.v1
Chicago/Turabian Style
Okada, H. 2023 "Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task – Part1: Evolution Strategy" Preprints. https://doi.org/10.20944/preprints202308.0081.v1
Abstract
Evolutionary algorithms find applicability in the 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. The author previously reported experimental evaluations on Evolution Strategy for reinforcement learning of neural networks, utilizing the pendulum control task. In this study, the Acrobot control task is adopted as another task. Experimental results demonstrate that ES successfully trained a Multi-Layer Perceptron to achieve a remarkable height of 99.85% concerning the maximum height. However, the trained MLP failed to maintain the chain end in an upright position throughout an episode. In this study, it was observed that employing 8 hidden units in the neural network yielded better results with statistical significance compared to using 4, 16, or 32 hidden units. Furthermore, the findings indicate that a larger population size in ES led to a more extensive exploration of potential solutions over a greater number of generations, which aligns with the previous study.
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.
