Constant Swap Regret in General-Sum Games via Optimistic Transition Matrices

Constant Swap Regret in General-Sum Games via Two-Scale Higher-Order Optimism

We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret in self-play, independent of the horizon $T$. With $n$ players and at most $m$ actions each, every player's individual swap regret is $O(\sqrt n\,m\...

A Horizon-Independent Regret Bound for Optimistic Hedge in General-Sum Games

This work proves that plain Optimistic Hedge with a constant step size can attain individual regret in general-sum games with n players and d=(d_1,\ldots,d_n) actions, under expected loss-vector feedback.

Constant regret in general games via higher-order optimism

We introduce an uncoupled learning algorithm which, when employed by all players of an arbitrary $N$-player normal form game with up to $K$ actions per player, guarantees $O(N^3\log^2 K)$ individual regret, uniformly over the horizon of play. The proposed algorithm - which we call higher-order optimism with discounting...

Constant Individual Regret in General Games

This work introduces \emph{ECHO-OFTRL}: optimistic follow-the-regularized-leader (OFTRL) equipped with an EMA cascade for high-order optimism (ECHO), where EMA denotes exponential moving average, and leverages a new form of optimism inspired by modern filter design.

Playing Against a DP-FTRL Learner in Repeated Zero-Sum Games

Fully Online Decentralized Learning in Stochastic Games with Unknown Independent Chains

We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs. We develop a fully online, decentralized, and uncoordinated mirror-descent algorithm that operates in the dual space of occupancy measures for approximating...