Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Curriculum Learning for Reinforcement Learning Domains: A Framework and
  Survey   Reinforcement learning (RL) is a popular paradigm for addressing sequential
decision tasks in which the agent has only limited environmental feedback.
Despite many advances over the past three decades, learning in many domains
still requires a large amount of interaction with the environment, which can be
prohibitively expensive in realistic scenarios. To address this problem,
transfer learning has been applied to reinforcement learning such that
experience gained in one task can be leveraged when starting to learn the next,
harder task. More recently, several lines of research have explored how tasks,
or data samples themselves, can be sequenced into a curriculum for the purpose
of learning a problem that may otherwise be too difficult to learn from
scratch. In this article, we present a framework for curriculum learning (CL)
in reinforcement learning, and use it to survey and classify existing CL
methods in terms of their assumptions, capabilities, and goals. Finally, we use
our framework to find open problems and suggest directions for future RL
curriculum learning research.

7
The categories of this paper are: hep-th
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an