46,951 research outputs found
Atari games and Intel processors
The asynchronous nature of the state-of-the-art reinforcement learning
algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes
them exceptionally suitable for CPU computations. However, given the fact that
deep reinforcement learning often deals with interpreting visual information, a
large part of the train and inference time is spent performing convolutions. In
this work we present our results on learning strategies in Atari games using a
Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0
machine learning framework. We also analyze effects of asynchronous
computations on the convergence of reinforcement learning algorithms
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
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