Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Survival of the strictest: Stable and unstable equilibria under
  regularized learning with partial information   In this paper, we examine the Nash equilibrium convergence properties of
no-regret learning in general N-player games. For concreteness, we focus on the
archetypal follow the regularized leader (FTRL) family of algorithms, and we
consider the full spectrum of uncertainty that the players may encounter - from
noisy, oracle-based feedback, to bandit, payoff-based information. In this
general context, we establish a comprehensive equivalence between the stability
of a Nash equilibrium and its support: a Nash equilibrium is stable and
attracting with arbitrarily high probability if and only if it is strict (i.e.,
each equilibrium strategy has a unique best response). This equivalence extends
existing continuous-time versions of the folk theorem of evolutionary game
theory to a bona fide algorithmic learning setting, and it provides a clear
refinement criterion for the prediction of the day-to-day behavior of no-regret
learning in games

26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph