16.2k citations · 16.4k across the 18 of their papers we have counts for
4 papers · 1 filter
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…
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…
Robust Multi-agent Counterfactual Prediction
Alexander Peysakhovich, Christian Kroer, Adam Lerer
We consider the problem of using logged data to make predictions about what would happen if we changed the `rules of the game' in a multi-agent system. This task is difficult becau…