Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning for Standards Design   Communications standards are designed via committees of humans holding
repeated meetings over months or even years until consensus is achieved. This
includes decisions regarding the modulation and coding schemes to be supported
over an air interface. We propose a way to "automate" the selection of the set
of modulation and coding schemes to be supported over a given air interface and
thereby streamline both the standards design process and the ease of extending
the standard to support new modulation schemes applicable to new higher-level
applications and services. Our scheme involves machine learning, whereby a
constructor entity submits proposals to an evaluator entity, which returns a
score for the proposal. The constructor employs reinforcement learning to
iterate on its submitted proposals until a score is achieved that was
previously agreed upon by both constructor and evaluator to be indicative of
satisfying the required design criteria (including performance metrics for
transmissions over the interface).

27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys