14 citations · 82 across the 18 of their papers we have counts for
27 papers
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
Anton Bakhtin, David J Wu, Adam Lerer +5
No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement l…
On Last-Iterate Convergence Beyond Zero-Sum Games
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina +1
Most existing results about \emph{last-iterate convergence} of learning dynamics are limited to two-player zero-sum games, and only apply under rigid assumptions about what dynamic…
Faster No-Regret Learning Dynamics for Extensive-Form Correlated and Coarse Correlated Equilibria
Ioannis Anagnostides, Gabriele Farina, Christian Kroer +2
A recent emerging trend in the literature on learning in games has been concerned with providing faster learning dynamics for correlated and coarse correlated equilibria in normal-…
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
Gabriele Farina, Chung-Wei Lee, Haipeng Luo +1
While extensive-form games (EFGs) can be converted into normal-form games (NFGs), doing so comes at the cost of an exponential blowup of the strategy space. So, progress on NFGs an…
Efficient Decentralized Learning Dynamics for Extensive-Form Coarse Correlated Equilibrium: No Expensive Computation of Stationary Distributions Required
Gabriele Farina, Andrea Celli, Tuomas Sandholm
While in two-player zero-sum games the Nash equilibrium is a well-established prescriptive notion of optimal play, its applicability as a prescriptive tool beyond that setting is l…
Simple Uncoupled No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
Gabriele Farina, Andrea Celli, Alberto Marchesi +1
The existence of simple uncoupled no-regret learning dynamics that converge to correlated equilibria in normal-form games is a celebrated result in the theory of multi-agent system…