Counterfactual Regret Minimization in Poker –

October 03, 2018Iulian Doroftei


Supercomputers employ “Counterfactual Regret Minimization.” Poker players can benefit from CRM, too.

A discussion of “Counterfactual Regret Minimization” and its potential application to poker players.

The phrase “Counterfactual Regret Minimization” may sound overly sophisticated to insert into a discussion of poker strategy. (Indeed, it probably is.) But luckily the concept to which it refers can be explained more simply, and in fact may prove to be quite useful to those struggling to play poker profitably and even to already successful players.

Counterfactual Regret Minimization or “CRM” refers to an algorithm used by neural networks to train at playing perfect poker. It bears a striking resemblance to heuristic strategies (that is, those based on experience) that human players employ when they try to learn poker by playing.

When winning poker pros are asked how have they managed to be consistently successful, their answers usually come in two varieties referring to two different learning systems: heuristic and analytical.

Those who jump right in at the poker table and learn the game by trial and error are adopting the heuristic approach. Meanwhile those who prefer first to analyze the game’s mechanics and strictly apply its mathematical principles at the tables, regardless of what their “intuition” might whisper into their ears, are taking the analytical approach.

Obviously, most highly accomplished players will employ both learning methods. That said, most of the poker pros — especially the old schoolers and the live grinders — prefer to learn heuristically by playing a high volume and thus developing an intuitively correct strategy, one which many of them cannot even explain in their own words.

When it comes to experience-based learning, the IT systems have a huge upper-hand over humans. They can acquire in a matter of

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