Learning to Bluff

TL;DR

This paper demonstrates how learning agents can develop bluffing strategies in poker through adaptive neural networks, enabling realistic and strategic gameplay without external guidance.

Contribution

It introduces a novel approach where neural network agents learn to bluff and call bluffs via a lambda learning algorithm, advancing AI in strategic game play.

Findings

Agents can learn to bluff through continuous adaptation.

Agents can identify and call opponents' bluffs.

Bluffing emerges as a strategic, optimized behavior.

Abstract

The act of bluffing confounds game designers to this day. The very nature of bluffing is even open for debate, adding further complication to the process of creating intelligent virtual players that can bluff, and hence play, realistically. Through the use of intelligent, learning agents, and carefully designed agent outlooks, an agent can in fact learn to predict its opponents reactions based not only on its own cards, but on the actions of those around it. With this wider scope of understanding, an agent can in learn to bluff its opponents, with the action representing not an illogical action, as bluffing is often viewed, but rather as an act of maximising returns through an effective statistical optimisation. By using a tee dee lambda learning algorithm to continuously adapt neural network agent intelligence, agents have been shown to be able to learn to bluff without outside prompting, and even to learn to call each others bluffs in free, competitive play.