arXiv AI
Jul 14
Evolutionarily Stable Stackelberg Equilibrium
arXiv:2603. 18385v3 Announce Type: replace-cross Abstract: We present a new solution concept called evolutionarily stable Stackelberg equilibrium (SESS).
Computing Evolutionarily Stable Strategies in Imperfect-Information Games
arXiv:2512. 10279v3 Announce Type: replace-cross Abstract: We present an algorithm for computing evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games of imperfect information.
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arXiv AI
Jun 30
Projected Exploitability Descent for Nash Equilibrium Computation in Multiplayer Imperfect-Information Games
arXiv:2606. 29169v1 Announce Type: cross Abstract: Many important games have more than two players and imperfect information.
arXiv AI
Jun 24
EMAgnet: Parameter-Space EMA Regularization for Policy Gradient Self-Play in Large Games
arXiv:2606. 23995v1 Announce Type: cross Abstract: Recent work has established that regularized policy gradient methods such as PPO, when used in self-play, can match or exceed specialized game-theoretic algorithms for solving two-player zero-sum imperfect-information games.
arXiv Machine Learning
Aug 11
Regret, equilibrium, and learning in games: A guided tour
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv AI
Jul 2
Quadratic Programming Approach for Nash Equilibrium Computation in Multiplayer Imperfect-Information Games
arXiv:2509. 25618v2 Announce Type: replace-cross Abstract: There has been significant recent progress in algorithms for approximation of Nash equilibrium in large two-player zero-sum imperfect-information games and exact computation of Nash equilibrium in multiplayer strategic-form games.
Hugging Face Trending Papers
Jul 7
FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game. Built on HiFight's minimalist 2D fighting game Footsies, it isolates the cyclic, non-transitive strategic interactions of fighting game neutral play while remaining simple enough for efficient analysis.
arXiv AI
Jun 30
Self-Play Reinforcement Learning under Imperfect Information in Big 2
arXiv:2605. 28863v2 Announce Type: replace-cross Abstract: Imperfect-information multiplayer games test whether agents can act under hidden information, sparse rewards, and non-stationary opponents.
arXiv Machine Learning
Jun 5
Regret Minimization with Adaptive Opponents in Repeated Games
arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.
arXiv AI
Jun 29
Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning
arXiv:2603. 00374v2 Announce Type: replace Abstract: Offline learning of strategies takes data efficiency to its extreme by restricting algorithms to a fixed dataset of state-action trajectories.
arXiv AI
Jul 8
FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
arXiv:2607. 06514v1 Announce Type: new Abstract: We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game.
arXiv Machine Learning
Aug 4
NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria
arXiv:2510. 18183v3 Announce Type: replace Abstract: Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning.
arXiv Machine Learning
Jun 17
Learning in Matching Games with Bandit Feedback
arXiv:2506. 03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match.