Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Index Selection with Structured Action Spaces   Configuration spaces for computer systems can be challenging for traditional
and automatic tuning strategies. Injecting task-specific knowledge into the
tuner for a task may allow for more efficient exploration of candidate
configurations. We apply this idea to the task of index set selection to
accelerate database workloads. Index set selection has been amenable to recent
applications of vanilla deep RL, but real deployments remain out of reach. In
this paper, we explore how learning index selection can be enhanced with
task-specific inductive biases, specifically by encoding these inductive biases
in better action structures. Index selection-specific action representations
arise when the problem is reformulated in terms of permutation learning and we
rely on recent work for learning RL policies on permutations. Through this
approach, we build an indexing agent that is able to achieve improved indexing
and validate its behavior with task-specific statistics. Early experiments
reveal that our agent can find configurations that are up to 40% smaller for
the same levels of latency as compared with other approaches and indicate more
intuitive indexing behavior.

7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin