Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning   Social psychology and real experiences show that cognitive consistency plays
an important role to keep human society in order: if people have a more
consistent cognition about their environments, they are more likely to achieve
better cooperation. Meanwhile, only cognitive consistency within a neighborhood
matters because humans only interact directly with their neighbors. Inspired by
these observations, we take the first step to introduce \emph{neighborhood
cognitive consistency} (NCC) into multi-agent reinforcement learning (MARL).
Our NCC design is quite general and can be easily combined with existing MARL
methods. As examples, we propose neighborhood cognition consistent deep
Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations.
Extensive experiments on several challenging tasks (i.e., packet routing, wifi
configuration, and Google football player control) justify the superior
performance of our methods compared with state-of-the-art MARL approaches.

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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
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The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga