Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Decentralized Learning Dynamics for Extensive-Form Coarse
  Correlated Equilibrium: No Expensive Computation of Stationary Distributions
  Required   While in two-player zero-sum games the Nash equilibrium is a well-established
prescriptive notion of optimal play, its applicability as a prescriptive tool
beyond that setting is limited. Consequently, the study of decentralized
learning dynamics that guarantee convergence to correlated solution concepts in
multiplayer, general-sum extensive-form (i.e., tree-form) games has become an
important topic of active research. The per-iteration complexity of the
currently known learning dynamics depends on the specific correlated solution
concept considered. For example, in the case of extensive-form correlated
equilibrium (EFCE), all known dynamics require, as an intermediate step at each
iteration, to compute the stationary distribution of multiple Markov chains, an
expensive operation in practice. Oppositely, in the case of normal-form coarse
correlated equilibrium (NFCCE), simple no-external-regret learning dynamics
that amount to a linear-time traversal of the tree-form decision space of each
agent suffice to guarantee convergence. This paper focuses on extensive-form
coarse correlated equilibrium (EFCCE), an intermediate solution concept that is
a subset of NFCCE and a superset of EFCE. Being a superset of EFCE, any
learning dynamics for EFCE automatically guarantees convergence to EFCCE.
However, since EFCCE is a simpler solution concept, this begs the question: do
learning dynamics for EFCCE that avoid the expensive computation of stationary
distributions exist? This paper answers the previous question in the positive.
Our learning dynamics only require the orchestration of no-external-regret
minimizers, thus showing that EFCCE is more akin to NFCCE than to EFCE from a
learning perspective. Our dynamics guarantees that the empirical frequency of
play after $T$ iteration is a $O(1/\sqrt{T})$-approximate EFCCE with high
probability, and an EFCCE almost surely in the limit.

14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph