98 citations · 333 across the 23 of their papers we have counts for
5 papers · 1 filter
Exploring the acceleration of Nekbone on reconfigurable architectures
Nick Brown
Hardware technological advances are struggling to match scientific ambition, and a key question is how we can use the transistors that we already have more effectively. This is esp…
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Jonathan Gray, Adam Lerer, Anton Bakhtin +1
Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that in…
Unlocking the Potential of Deep Counterfactual Value Networks
Ryan Zarick, Bryan Pellegrino, Noam Brown +1
Deep counterfactual value networks combined with continual resolving provide a way to conduct depth-limited search in imperfect-information games. However, since their introduction…
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…
DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
Eric Steinberger, Adam Lerer, Noam Brown
We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash E…