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pro vyhledávání: '"Fogelman, Bryden"'
Autor:
Fetterman, Abraham J., Kitanidis, Ellie, Albrecht, Joshua, Polizzi, Zachary, Fogelman, Bryden, Knutins, Maksis, Wróblewski, Bartosz, Simon, James B., Qiu, Kanjun
Hyperparameter tuning of deep learning models can lead to order-of-magnitude performance gains for the same amount of compute. Despite this, systematic tuning is uncommon, particularly for large models, which are expensive to evaluate and tend to hav
Externí odkaz:
http://arxiv.org/abs/2306.08055
Autor:
Albrecht, Joshua, Fetterman, Abraham J., Fogelman, Bryden, Kitanidis, Ellie, Wróblewski, Bartosz, Seo, Nicole, Rosenthal, Michael, Knutins, Maksis, Polizzi, Zachary, Simon, James B., Qiu, Kanjun
Despite impressive successes, deep reinforcement learning (RL) systems still fall short of human performance on generalization to new tasks and environments that differ from their training. As a benchmark tailored for studying RL generalization, we i
Externí odkaz:
http://arxiv.org/abs/2210.13417