Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning in Ice Hockey for Context-Aware Player
  Evaluation   A variety of machine learning models have been proposed to assess the
performance of players in professional sports. However, they have only a
limited ability to model how player performance depends on the game context.
This paper proposes a new approach to capturing game context: we apply Deep
Reinforcement Learning (DRL) to learn an action-value Q function from 3M
play-by-play events in the National Hockey League (NHL). The neural network
representation integrates both continuous context signals and game history,
using a possession-based LSTM. The learned Q-function is used to value players'
actions under different game contexts. To assess a player's overall
performance, we introduce a novel Game Impact Metric (GIM) that aggregates the
values of the player's actions. Empirical Evaluation shows GIM is consistent
throughout a play season, and correlates highly with standard success measures
and future salary.

26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
2
The categories of this paper are: math