![]() Go is the opposite of Atari games to some extent: while the game has perfect information, the challenge comes from the strategic interaction of multiple agents. A Deep Q-network learns how to play under the reinforcement learning framework, where a single agent interacts with a fixed environment, possibly with imperfect information.Īlso in 2015, DeepMind's AlphaGo used similar deep reinforcement learning techniques to beat professionals at Go for the first time in history. In 2015, DeepMind’s Deep Q-network mastered a number of Atari games. Libratus is not the only game-playing AI to make recent news headlines, but it is uniquely impressive. This was the first AI agent to beat professional players in heads-up no-limit Texas hold ’em. Libratus eventually won by a staggering 14.7 big blinds per 100 hands, trouncing the world’s top poker professionals with 99.98% statistical significance. The official competition between human and machine took place over three weeks, but it was clear that the computer was king after only a few days of play. Instead, they were fighting against a common foe: an AI system called Libratus that was developed by Carnegie Mellon researchers Noam Brown and Tuomas Sandholm. They were not competing against each other. ![]() In January 2017, four world-class poker players engaged in a three-week battle of heads-up no-limit Texas hold ’em.
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