Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning with Double Q-learning   The popular Q-learning algorithm is known to overestimate action values under
certain conditions. It was not previously known whether, in practice, such
overestimations are common, whether they harm performance, and whether they can
generally be prevented. In this paper, we answer all these questions
affirmatively. In particular, we first show that the recent DQN algorithm,
which combines Q-learning with a deep neural network, suffers from substantial
overestimations in some games in the Atari 2600 domain. We then show that the
idea behind the Double Q-learning algorithm, which was introduced in a tabular
setting, can be generalized to work with large-scale function approximation. We
propose a specific adaptation to the DQN algorithm and show that the resulting
algorithm not only reduces the observed overestimations, as hypothesized, but
that this also leads to much better performance on several games.

13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th