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- W4387651521 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. The author previously reported experimental evaluations on Evolution Strategy for reinforcement learning of neural networks, utilizing the Acrobot control task. In this study, Genetic Algorithm is adopted as another instance of major evolutionary algorithms. Experimental results demonstrate that there was no statistically significant difference between the experimental performances of GA and ES, but the priority of generations and offsprings was different; GA performed better with a greater number of generations while ES performed better with a greater number of offsprings. Eight hidden units were the best among four variations (4, 8, 16 or 32 units), which aligns with previous study using ES." @default.
- W4387651521 created "2023-10-16" @default.
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- W4387651521 date "2023-10-13" @default.
- W4387651521 modified "2023-10-16" @default.
- W4387651521 title "Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task — Part2: Genetic Algorithm" @default.
- W4387651521 doi "https://doi.org/10.20944/preprints202310.0852.v1" @default.
- W4387651521 hasPublicationYear "2023" @default.
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