9,918 research outputs found
Flow Navigation by Smart Microswimmers via Reinforcement Learning
Smart active particles can acquire some limited knowledge of the fluid
environment from simple mechanical cues and exert a control on their preferred
steering direction. Their goal is to learn the best way to navigate by
exploiting the underlying flow whenever possible. As an example, we focus our
attention on smart gravitactic swimmers. These are active particles whose task
is to reach the highest altitude within some time horizon, given the
constraints enforced by fluid mechanics. By means of numerical experiments, we
show that swimmers indeed learn nearly optimal strategies just by experience. A
reinforcement learning algorithm allows particles to learn effective strategies
even in difficult situations when, in the absence of control, they would end up
being trapped by flow structures. These strategies are highly nontrivial and
cannot be easily guessed in advance. This Letter illustrates the potential of
reinforcement learning algorithms to model adaptive behavior in complex flows
and paves the way towards the engineering of smart microswimmers that solve
difficult navigation problems.Comment: Published on Physical Review Letters (April 12, 2017
Learning to Skim Text
Recurrent Neural Networks are showing much promise in many sub-areas of
natural language processing, ranging from document classification to machine
translation to automatic question answering. Despite their promise, many
recurrent models have to read the whole text word by word, making it slow to
handle long documents. For example, it is difficult to use a recurrent network
to read a book and answer questions about it. In this paper, we present an
approach of reading text while skipping irrelevant information if needed. The
underlying model is a recurrent network that learns how far to jump after
reading a few words of the input text. We employ a standard policy gradient
method to train the model to make discrete jumping decisions. In our benchmarks
on four different tasks, including number prediction, sentiment analysis, news
article classification and automatic Q\&A, our proposed model, a modified LSTM
with jumping, is up to 6 times faster than the standard sequential LSTM, while
maintaining the same or even better accuracy
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