98 citations · 333 across the 23 of their papers we have counts for
9 papers · 1 filter
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
Samuel Sokota, Ryan D'Orazio, Chun Kai Ling +3
In their seminal work, Nayyar et al. (2013) showed that imperfect information can be abstracted away from common-payoff games by having players publicly announce their policies as…
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…
Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization
Hugh Zhang, Adam Lerer, Noam Brown
We extend the classic regret minimization framework for approximating equilibria in normal-form games by greedily weighing iterates based on regrets observed at runtime. Theoretica…
Safe Search for Stackelberg Equilibria in Extensive-Form Games
Chun Kai Ling, Noam Brown
Stackelberg equilibrium is a solution concept in two-player games where the leader has commitment rights over the follower. In recent years, it has become a cornerstone of many sec…
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Noam Brown, Anton Bakhtin, Adam Lerer +1
The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of successes in single-agent settings and p…
Stable-Predictive Optimistic Counterfactual Regret Minimization
Gabriele Farina, Christian Kroer, Noam Brown +1
The CFR framework has been a powerful tool for solving large-scale extensive-form games in practice. However, the theoretical rate at which past CFR-based algorithms converge to th…
Solving Imperfect-Information Games via Discounted Regret Minimization
Noam Brown, Tuomas Sandholm
Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to approximately solving large imperfect-i…
Depth-Limited Solving for Imperfect-Information Games
Noam Brown, Tuomas Sandholm, Brandon Amos
A fundamental challenge in imperfect-information games is that states do not have well-defined values. As a result, depth-limited search algorithms used in single-agent settings an…
Reduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning
Noam Brown, Tuomas Sandholm
Counterfactual Regret Minimization (CFR) is the most popular iterative algorithm for solving zero-sum imperfect-information games. Regret-Based Pruning (RBP) is an improvement that…