Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dueling Network Architectures for Deep Reinforcement Learning   In recent years there have been many successes of using deep representations
in reinforcement learning. Still, many of these applications use conventional
architectures, such as convolutional networks, LSTMs, or auto-encoders. In this
paper, we present a new neural network architecture for model-free
reinforcement learning. Our dueling network represents two separate estimators:
one for the state value function and one for the state-dependent action
advantage function. The main benefit of this factoring is to generalize
learning across actions without imposing any change to the underlying
reinforcement learning algorithm. Our results show that this architecture leads
to better policy evaluation in the presence of many similar-valued actions.
Moreover, the dueling architecture enables our RL agent to outperform the
state-of-the-art on the Atari 2600 domain.

19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn