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Found 8 papers, 0 papers with code
Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property
Policy gradient methods enjoy strong practical performance in numerous tasks in reinforcement learning.
Near-Optimal $Φ$-Regret Learning in Extensive-Form Games
In this paper, we establish efficient and uncoupled learning dynamics so that, when employed by all players in multiplayer perfect-recall imperfect-information extensive-form games, the trigger regret of each player grows as $O(\log T)$ after $T$ repetitions of play.
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
In this work, we depart from those prior results by investigating infinite-horizon \emph{adversarial team Markov games}, a natural and well-motivated class of games in which a team of identically-interested players -- in the absence of any explicit coordination or communication -- is competing against an adversarial player.
Near-Optimal No-Regret Learning Dynamics for General Convex Games
In this paper, we answer this in the positive by establishing the first uncoupled learning algorithm with $O(\log T)$ per-player regret in general \emph{convex games}, that is, games with concave utility functions supported on arbitrary convex and compact strategy sets.
Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games
In this paper we establish efficient and \emph{uncoupled} learning dynamics so that, when employed by all players in a general-sum multiplayer game, the \emph{swap regret} of each player after $T$ repetitions of the game is bounded by $O(\log T)$, improving over the prior best bounds of $O(\log^4 (T))$.
Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games
Recently, Daskalakis, Fishelson, and Golowich (DFG) (NeurIPS`21) showed that if all agents in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights Update (OMWU), the external regret of every player is $O(\textrm{polylog}(T))$ after $T$ repetitions of the game.
Faster No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
A recent emerging trend in the literature on learning in games has been concerned with providing accelerated learning dynamics for correlated and coarse correlated equilibria in normal-form games.
Robust Learning under Strong Noise via SQs
This work provides several new insights on the robustness of Kearns' statistical query framework against challenging label-noise models.
