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Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
[article]
2020
arXiv
pre-print
The landmark achievements of AlphaGo Zero have created great research interest into self-play in reinforcement learning. In self-play, Monte Carlo Tree Search is used to train a deep neural network, that is then used in tree searches. Training itself is governed by many hyperparameters.There has been surprisingly little research on design choices for hyper-parameter values and loss-functions, presumably because of the prohibitive computational cost to explore the parameter space. In this paper,
arXiv:2003.05988v1
fatcat:y7mtudj3q5anbesfnviwtxd3pq