Computing Human-Understandable Strategies
This work addresses the challenge of making complex game strategies accessible to human players, though it is incremental as it builds on existing equilibrium computation methods.
The paper tackles the problem of computing equilibrium strategies in poker that are understandable and implementable by humans, given computational and memory limitations, and presents algorithms that learn from training instances to derive new fundamental rules for poker strategy.
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.