Constant Swap Regret in General-Sum Games via Two-Scale Higher-Order Optimism
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2609. 16751v1 Announce Type: cross Abstract: We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret, independent of the horizon $T$.
We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret, independent of the horizon...
arXiv:2608. 04149v1 Announce Type: cross Abstract: Swap regret governs the rate at which uncoupled learning dynamics converge to correlated equilibria in multiplayer general-sum games.
The paper presents an uncoupled learning algorithm, higher-order optimism with discounting (HOOD), for arbitrary N-player normal form games with up to K actions per player. HOOD achieves an individual regret bound of O(N³ log² K) uniformly over the play horizon by combining a discounted (N+1)-th order predictor with entropic regularization over a lifted strategy space. This design mitigates large oscillations in play, addressing a key challenge in prior attempts to attain constant regret in general games.
arXiv:2607. 23333v1 Announce Type: cross Abstract: We revisit the regret loss framework introduced in Park et al.
arXiv:2608. 31166v1 Announce Type: new Abstract: Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon.