A bi-level algorithm adapts scalarization weights online in vector-valued repeated games to obtain sublinear regret bounds and raise convergence to a preferred equilibrium from roughly 50% to 80%.
Faster game solving via predictive blackwell approachability: Connecting regret matching and mirror descent
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.GT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Online Scalarization in Vector-Valued Games
A bi-level algorithm adapts scalarization weights online in vector-valued repeated games to obtain sublinear regret bounds and raise convergence to a preferred equilibrium from roughly 50% to 80%.