In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to tune the ...
Mar 2, 2020 · In this paper, we propose to use RL, specifically the Q- learning algorithm, to tune the structural hyper-parameter of. HSES, i.e. the number of ...
Jul 19, 2020 · In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to ...
A reinforcement learning mechanism is applied to tune the hyperparameters to control computational resource allocation problems [32] . Complete insights into ...
In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to tune the ...
In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to tune the ...
After estimating the second-stage parameters, the proposed adaptive Q-learning procedure follows the same remaining steps as in standard Q-learning. We prove a ...
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Both of our experiments show that Q-AdamR outperforms Q-Adam, vanilla Q-learning and DQN in terms of convergence speed and variance reduction. Compared with DQN ...
A framework for adaptive parameter control for hybrid EAs is proposed, in which the switching time is controlled by a learned agent rather than a manually ...