Apr 24, 2019 · In this paper we propose to optimize the parameters of neural networks by means of estimation of distribution algorithms.
Apr 25, 2019 · More precisely, the univariate marginal distribution algorithm is used for evolving neural networks in various reinforcement learning tasks. For ...
This paper proposes to optimize the parameters of neural networks by means of estimation of distribution algorithms, using the univariate marginal ...
More precisely, the univariate marginal distribution algorithm is used for evolving neural networks in various reinforcement learning tasks. For the sake of ...
Co-authors ; Evolving neural networks in reinforcement learning by means of UMDAc. M Malagon, J Ceberio. arXiv preprint arXiv:1904.10932, 2019. 5, 2019.
Co-authors ; Evolving neural networks in reinforcement learning by means of UMDAc. M Malagon, J Ceberio. arXiv preprint arXiv:1904.10932, 2019. 5, 2019.
Evolving Neural Networks in Reinforcement Learning by means of UMDAc · no code implementations • 24 Apr 2019 • Mikel Malagon, Josu Ceberio.
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Figure 4: NN parameter treatment in order to apply UMDA c . Evolving Neural Networks in Reinforcement Learning by means of UMDAc. Preprint. Full-text ...
Nov 14, 2018 · The biggest difference between Evolutionary Strategies and Reinforcement Learning is that ES is a global optimization technique while RL is a local ...
Apr 7, 2021 · Hi everyone,. I have a neural network, which I parametrize with different random weights for creating several variants of it.