Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement
  Learning   Many real-world scenarios involve a team of agents that have to coordinate
their policies to achieve a shared goal. Previous studies mainly focus on
decentralized control to maximize a common reward and barely consider the
coordination among control policies, which is critical in dynamic and
complicated environments. In this work, we propose factorizing the joint team
policy into a graph generator and graph-based coordinated policy to enable
coordinated behaviours among agents. The graph generator adopts an
encoder-decoder framework that outputs directed acyclic graphs (DAGs) to
capture the underlying dynamic decision structure. We also apply the
DAGness-constrained and DAG depth-constrained optimization in the graph
generator to balance efficiency and performance. The graph-based coordinated
policy exploits the generated decision structure. The graph generator and
coordinated policy are trained simultaneously to maximize the discounted
return. Empirical evaluations on Collaborative Gaussian Squeeze, Cooperative
Navigation, and Google Research Football demonstrate the superiority of the
proposed method.

27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph