Deep Reinforcement Learning from Self Play in Imperfect Information Games
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.
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