arXiv:2607. 17543v1 Announce Type: new Abstract: In two-player zero-sum games whose Nash equilibria form a convex set, regularized solvers such as Regularized Nash Dynamics (R-NaD) empirically select the maximum-entropy member: the information projection (I-projection) of a uniform reference onto the Nash set.
By Luis Leal
arXiv:2606. 29169v1 Announce Type: cross Abstract: Many important games have more than two players and imperfect information.
By Sam Ganzfried
The paper investigates how a reference policy can be used to steer regularized self‑play toward a specific equilibrium in two‑player zero‑sum games. By anchoring the reference at a target equilibrium and refining the self‑play process, the authors achieve precise convergence to that target with very low exploitability and coordinate error. The study also explores the effects of off‑manifold references, mirror‑step sizing, and boundary saturation on selection accuracy.
By Luis Leal
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.
By Taira Tsuchiya
arXiv:2606. 01159v1 Announce Type: new Abstract: We study two-player zero-sum games (TPZSGs) with bandit feedback under fairness constraints requiring every action to be played with probability at least $\alpha/m$.
By S Akash, Pratik Gajane
arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.
By Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du