Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

2016-03-03 · Johannes Heinrich, David Silver

Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge. Our method combines fictitious self-play with deep reinforcement learning. When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged. In Limit Texas Holdem, a poker game of real-world scale, NFSP learnt a strategy that approached the performance of state-of-the-art, superhuman algorithms based on significant domain expertise.

📄 PDF Abstract BibTeX arXiv:1603.01121

Code (7)

EricSteinberger/DREAM

IAARhub/TrucoAnalytics

TinkeringCode/Neural-Fictitous-Self-Play pytorch

deepmind/open_spiel

heidekrueger/bnelearn pytorch

jsanderink/tue pytorch

quantumiracle/mars pytorch

Tasks

Card GamesDeep Reinforcement LearningGame of Pokerreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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