Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contextualize Me -- The Case for Context in Reinforcement Learning   While Reinforcement Learning (RL) has made great strides towards solving
increasingly complicated problems, many algorithms are still brittle to even
slight changes in environments. Contextual Reinforcement Learning (cRL)
provides a theoretical framework to model such changes in a principled manner,
thereby enabling flexible, precise and interpretable task specification and
generation. Thus, cRL is an important formalization for studying generalization
in RL. In this work, we reason about solving cRL in theory and practice. We
show that theoretically optimal behavior in contextual Markov Decision
Processes requires explicit context information. In addition, we empirically
explore context-based task generation, utilizing context information in
training and propose cGate, our state-modulating policy architecture. To this
end, we introduce the first benchmark library designed for generalization based
on cRL extensions of popular benchmarks, CARL. In short: Context matters!

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The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics