98 citations · 333 across the 23 of their papers we have counts for
6 papers · 1 filter
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
Athul Paul Jacob, David J. Wu, Gabriele Farina +5
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predic…
No-Press Diplomacy from Scratch
Anton Bakhtin, David Wu, Adam Lerer +1
Prior AI successes in complex games have largely focused on settings with at most hundreds of actions at each decision point. In contrast, Diplomacy is a game with more than 10^20…
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Arnaud Fickinger, Hengyuan Hu, Brandon Amos +2
Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker. However, the search methods used in these games, and in many ot…
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
Hengyuan Hu, Adam Lerer, Noam Brown +1
Search is an important tool for computing effective policies in single- and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark…
Off-Belief Learning
Hengyuan Hu, Adam Lerer, Brandon Cui +4
The standard problem setting in Dec-POMDPs is self-play, where the goal is to find a set of policies that play optimally together. Policies learned through self-play may adopt arbi…
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…