Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RLlib: Abstractions for Distributed Reinforcement Learning   Reinforcement learning (RL) algorithms involve the deep nesting of highly
irregular computation patterns, each of which typically exhibits opportunities
for distributed computation. We argue for distributing RL components in a
composable way by adapting algorithms for top-down hierarchical control,
thereby encapsulating parallelism and resource requirements within
short-running compute tasks. We demonstrate the benefits of this principle
through RLlib: a library that provides scalable software primitives for RL.
These primitives enable a broad range of algorithms to be implemented with high
performance, scalability, and substantial code reuse. RLlib is available at
https://rllib.io/.

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The categories of this paper are: comp-gas
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: q-fin