449 research outputs found
Robust approachability and regret minimization in games with partial monitoring
Approachability has become a standard tool in analyzing earning algorithms in
the adversarial online learning setup. We develop a variant of approachability
for games where there is ambiguity in the obtained reward that belongs to a
set, rather than being a single vector. Using this variant we tackle the
problem of approachability in games with partial monitoring and develop simple
and efficient algorithms (i.e., with constant per-step complexity) for this
setup. We finally consider external regret and internal regret in repeated
games with partial monitoring and derive regret-minimizing strategies based on
approachability theory
Calibration and Internal no-Regret with Partial Monitoring
Calibrated strategies can be obtained by performing strategies that have no
internal regret in some auxiliary game. Such strategies can be constructed
explicitly with the use of Blackwell's approachability theorem, in an other
auxiliary game. We establish the converse: a strategy that approaches a convex
-set can be derived from the construction of a calibrated strategy. We
develop these tools in the framework of a game with partial monitoring, where
players do not observe the actions of their opponents but receive random
signals, to define a notion of internal regret and construct strategies that
have no such regret
Approachability in unknown games: Online learning meets multi-objective optimization
In the standard setting of approachability there are two players and a target
set. The players play repeatedly a known vector-valued game where the first
player wants to have the average vector-valued payoff converge to the target
set which the other player tries to exclude it from this set. We revisit this
setting in the spirit of online learning and do not assume that the first
player knows the game structure: she receives an arbitrary vector-valued reward
vector at every round. She wishes to approach the smallest ("best") possible
set given the observed average payoffs in hindsight. This extension of the
standard setting has implications even when the original target set is not
approachable and when it is not obvious which expansion of it should be
approached instead. We show that it is impossible, in general, to approach the
best target set in hindsight and propose achievable though ambitious
alternative goals. We further propose a concrete strategy to approach these
goals. Our method does not require projection onto a target set and amounts to
switching between scalar regret minimization algorithms that are performed in
episodes. Applications to global cost minimization and to approachability under
sample path constraints are considered
A Geometric Proof of Calibration
We provide yet another proof of the existence of calibrated forecasters; it
has two merits. First, it is valid for an arbitrary finite number of outcomes.
Second, it is short and simple and it follows from a direct application of
Blackwell's approachability theorem to carefully chosen vector-valued payoff
function and convex target set. Our proof captures the essence of existing
proofs based on approachability (e.g., the proof by Foster, 1999 in case of
binary outcomes) and highlights the intrinsic connection between
approachability and calibration
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