Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Attentional Communication for Multi-Agent Cooperation   Communication could potentially be an effective way for multi-agent
cooperation. However, information sharing among all agents or in predefined
communication architectures that existing methods adopt can be problematic.
When there is a large number of agents, agents cannot differentiate valuable
information that helps cooperative decision making from globally shared
information. Therefore, communication barely helps, and could even impair the
learning of multi-agent cooperation. Predefined communication architectures, on
the other hand, restrict communication among agents and thus restrain potential
cooperation. To tackle these difficulties, in this paper, we propose an
attentional communication model that learns when communication is needed and
how to integrate shared information for cooperative decision making. Our model
leads to efficient and effective communication for large-scale multi-agent
cooperation. Empirically, we show the strength of our model in a variety of
cooperative scenarios, where agents are able to develop more coordinated and
sophisticated strategies than existing methods.

16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc