14,047 research outputs found
Continual Reinforcement Learning in 3D Non-stationary Environments
High-dimensional always-changing environments constitute a hard challenge for
current reinforcement learning techniques. Artificial agents, nowadays, are
often trained off-line in very static and controlled conditions in simulation
such that training observations can be thought as sampled i.i.d. from the
entire observations space. However, in real world settings, the environment is
often non-stationary and subject to unpredictable, frequent changes. In this
paper we propose and openly release CRLMaze, a new benchmark for learning
continually through reinforcement in a complex 3D non-stationary task based on
ViZDoom and subject to several environmental changes. Then, we introduce an
end-to-end model-free continual reinforcement learning strategy showing
competitive results with respect to four different baselines and not requiring
any access to additional supervised signals, previously encountered
environmental conditions or observations.Comment: Accepted in the CLVision Workshop at CVPR2020: 13 pages, 4 figures, 5
table
Assessing hyper parameter optimization and speedup for convolutional neural networks
The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures
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