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 Equilibrium in two-player zero-sum games and to an extensive-form coarse correlated equilibrium in all other games. Our primary innovation is an effective algorithm that, in contrast to other regret-based deep learning algorithms, does not require access to a perfect simulator of the game to achieve good performance. We show that DREAM empirically achieves state-of-the-art performance among model-free algorithms in popular benchmark games, and is even competitive with algorithms that do use a perfect simulator.
@article{arxiv.2006.10410,
title = {DREAM: Deep Regret minimization with Advantage baselines and Model-free learning},
author = {Eric Steinberger and Adam Lerer and Noam Brown},
journal= {arXiv preprint arXiv:2006.10410},
year = {2020}
}