Machine learning goes beyond theory to beat human poker champs ZDNet

Among the many achievements of machine learning in recent years, some of the most striking are the victories of the machine against human players in games, such as Google's DeepMind group's conquest of Go in 2016. In such milestones, researchers are often guided by theoretical math that says there can be an optimal strategy to be found, given a good algorithm and enough compute. But what do you do when theory breaks down? Two researchers at Carnegie Mellon University and Facebook went back to the drawing board to solve "heads-up no-limit Texas hold'em," the most popular form of multiplayer poker in the world. Theory isn't computable for this form of the card game, so they designed some elegant search strategies for their computer program, "Pluribus," to beat the best human players in 10,000 hands of poker.