Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-world Video Adaptation with Reinforcement Learning   Client-side video players employ adaptive bitrate (ABR) algorithms to
optimize user quality of experience (QoE). We evaluate recently proposed
RL-based ABR methods in Facebook's web-based video streaming platform.
Real-world ABR contains several challenges that requires customized designs
beyond off-the-shelf RL algorithms -- we implement a scalable neural network
architecture that supports videos with arbitrary bitrate encodings; we design a
training method to cope with the variance resulting from the stochasticity in
network conditions; and we leverage constrained Bayesian optimization for
reward shaping in order to optimize the conflicting QoE objectives. In a
week-long worldwide deployment with more than 30 million video streaming
sessions, our RL approach outperforms the existing human-engineered ABR
algorithms.

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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: econ
19
The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
2
The categories of this paper are: math
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The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc