Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modular Multi-Objective Deep Reinforcement Learning with Decision Values   In this work we present a method for using Deep Q-Networks (DQNs) in
multi-objective environments. Deep Q-Networks provide remarkable performance in
single objective problems learning from high-level visual state
representations. However, in many scenarios (e.g in robotics, games), the agent
needs to pursue multiple objectives simultaneously. We propose an architecture
in which separate DQNs are used to control the agent's behaviour with respect
to particular objectives. In this architecture we introduce decision values to
improve the scalarization of multiple DQNs into a single action. Our
architecture enables the decomposition of the agent's behaviour into
controllable and replaceable sub-behaviours learned by distinct modules.
Moreover, it allows to change the priorities of particular objectives
post-learning, while preserving the overall performance of the agent. To
evaluate our solution we used a game-like simulator in which an agent -
provided with high-level visual input - pursues multiple objectives in a 2D
world.

36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc