Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration   Many reality tasks such as robot coordination can be naturally modelled as
multi-agent cooperative system where the rewards are sparse. This paper focuses
on learning decentralized policies for such tasks using sub-optimal
demonstration. To learn the multi-agent cooperation effectively and tackle the
sub-optimality of demonstration, a self-improving learning method is proposed:
On the one hand, the centralized state-action values are initialized by the
demonstration and updated by the learned decentralized policy to improve the
sub-optimality. On the other hand, the Nash Equilibrium are found by the
current state-action value and are used as a guide to learn the policy. The
proposed method is evaluated on the combat RTS games which requires a high
level of multi-agent cooperation. Extensive experimental results on various
combat scenarios demonstrate that the proposed method can learn multi-agent
cooperation effectively. It significantly outperforms many state-of-the-art
demonstration based approaches.

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The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con