Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable
  Grid Environments   In this paper, we consider the problem of multi-agent navigation in partially
observable grid environments. This problem is challenging for centralized
planning approaches as they, typically, rely on the full knowledge of the
environment. We suggest utilizing the reinforcement learning approach when the
agents, first, learn the policies that map observations to actions and then
follow these policies to reach their goals. To tackle the challenge associated
with learning cooperative behavior, i.e. in many cases agents need to yield to
each other to accomplish a mission, we use a mixing Q-network that complements
learning individual policies. In the experimental evaluation, we show that such
approach leads to plausible results and scales well to large number of agents.

11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph