Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning and Solving Many-Player Games through a Cluster-Based
  Representation   In addressing the challenge of exponential scaling with the number of agents
we adopt a cluster-based representation to approximately solve asymmetric games
of very many players. A cluster groups together agents with a similar
"strategic view" of the game. We learn the clustered approximation from data
consisting of strategy profiles and payoffs, which may be obtained from
observations of play or access to a simulator. Using our clustering we
construct a reduced "twins" game in which each cluster is associated with two
players of the reduced game. This allows our representation to be individually-
responsive because we align the interests of every individual agent with the
strategy of its cluster. Our approach provides agents with higher payoffs and
lower regret on average than model-free methods as well as previous
cluster-based methods, and requires only few observations for learning to be
successful. The "twins" approach is shown to be an important component of
providing these low regret approximations.

25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con