Computing Human-Understandable Strategies

TL;DR

This paper develops algorithms to compute human-understandable strategies for poker, enabling strategies that are simple enough for humans to implement and remember, based on learning from diverse game instances.

Contribution

It introduces a method to generate and learn strategies that are both effective and human-understandable in complex poker games with arbitrary information distributions.

Findings

Learned fundamental rules for human-implementable poker strategies

Algorithms perform well on unseen information distributions

Strategies are simple enough for human implementation

Abstract

Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that can actually be implemented by humans, who may have computational limitations and may only be able to remember a small number of features or components of the strategies that have been computed. We study poker games where private information distributions can be arbitrary. We create a large training set of game instances and solutions, by randomly selecting the information probabilities, and present algorithms that learn from the training instances in order to perform well in games with unseen information distributions. We are able to conclude several new fundamental rules about poker strategy that can be easily implemented by humans.