2016 DeepReinforcementLearningfromSe

• (Heinrich & Silver, 2016) ⇒ Johannes Heinrich, and David Silver. (2016). “Deep Reinforcement Learning from Self-play in Imperfect-information Games.” In: Proceedings of NIPS Deep Reinforcement Learning Workshop.

Subject Headings: Self-Play Reinforcement Learning, Video Game Playing Strategy.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Deep+Reinforcement+Learning+from+Self-play+in+Imperfect-information+Games

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 DeepReinforcementLearningfromSe	David Silver Johannes Heinrich			Deep Reinforcement Learning from Self-play in Imperfect-information Games