Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Atari Grand Challenge Dataset   Recent progress in Reinforcement Learning (RL), fueled by its combination,
with Deep Learning has enabled impressive results in learning to interact with
complex virtual environments, yet real-world applications of RL are still
scarce. A key limitation is data efficiency, with current state-of-the-art
approaches requiring millions of training samples. A promising way to tackle
this problem is to augment RL with learning from human demonstrations. However,
human demonstration data is not yet readily available. This hinders progress in
this direction. The present work addresses this problem as follows. We (i)
collect and describe a large dataset of human Atari 2600 replays -- the largest
and most diverse such data set publicly released to date, (ii) illustrate an
example use of this dataset by analyzing the relation between demonstration
quality and imitation learning performance, and (iii) outline possible research
directions that are opened up by our work.

16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys