12 citations · 22 across the 14 of their papers we have counts for
16 papers
Superhuman AI for Stratego Using Self-Play Reinforcement Learning and Test-Time Search
Samuel Sokota, Eugene Vinitsky, Hengyuan Hu +2
Few classical games have been regarded as such significant benchmarks of artificial intelligence as to have justified training costs in the millions of dollars. Among these, Strate…
Reevaluating Policy Gradient Methods for Imperfect-Information Games
Max Rudolph, Nathan Lichtle, Sobhan Mohammadpour +6
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed nu…
Computing Low-Entropy Couplings for Large-Support Distributions
Samuel Sokota, Dylan Sam, Christian Schroeder de Witt +3
Minimum-entropy coupling (MEC) -- the process of finding a joint distribution with minimum entropy for given marginals -- has applications in areas such as causality and steganogra…
Neural Functional Transformers
Allan Zhou, Kaien Yang, Yiding Jiang +5
The recent success of neural networks as implicit representation of data has driven growing interest in neural functionals: models that can process other neural networks as input b…
The Update-Equivalence Framework for Decision-Time Planning
Samuel Sokota, Gabriele Farina, David J. Wu +4
The process of revising (or constructing) a policy at execution time -- known as decision-time planning -- has been key to achieving superhuman performance in perfect-information g…
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning
Yat Long Lo, Christian Schroeder de Witt, Samuel Sokota +2
By enabling agents to communicate, recent cooperative multi-agent reinforcement learning (MARL) methods have demonstrated better task performance and more coordinated behavior. Mos…