6 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
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
Corrigendum to "Robust approachability and regret minimization in games with partial monitoring"
This short note provides a patch for an incorrect statement in the conference article http://hal.archives-ouvertes.fr/hal-0059569