Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey on Recent Advances and Challenges in Reinforcement
  LearningMethods for Task-Oriented Dialogue Policy Learning   Dialogue Policy Learning is a key component in a task-oriented dialogue
system (TDS) that decides the next action of the system given the dialogue
state at each turn. Reinforcement Learning (RL) is commonly chosen to learn the
dialogue policy, regarding the user as the environment and the system as the
agent. Many benchmark datasets and algorithms have been created to facilitate
the development and evaluation of dialogue policy based on RL. In this paper,
we survey recent advances and challenges in dialogue policy from the
prescriptive of RL. More specifically, we identify the major problems and
summarize corresponding solutions for RL-based dialogue policy learning.
Besides, we provide a comprehensive survey of applying RL to dialogue policy
learning by categorizing recent methods into basic elements in RL. We believe
this survey can shed a light on future research in dialogue management.

16
The categories of this paper are: stat
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph