AI for Imperfect-Information Games: Beating Top Humans in No-Limit Poker

Despite AI successes in perfect-information games, the hidden information and large size of no-limit poker have made the game difficult for AI to tackle. Libratus is an AI that, in a 120,000-hand competition, defeated four top pros in heads-up no-limit Texas hold’em poker, the leading benchmark in imperfect-information game solving. This talk explains why imperfect-information games are fundamentally more difficult than perfect-information games, and the advances in Libratus that overcame those challenges. In particular, this talk describes new methods for real-time planning in imperfect-information games that have theoretical guarantees. Additional research has extended these methods to deeper game trees, enabling the development of the master-level poker AI Modicum which was constructed using only a 4-core CPU and 16 GB of RAM. These algorithms are domain-independent and can be applied to a variety of strategic interactions involving hidden information.

Speaker Details

Noam Brown is a PhD student in computer science at Carnegie Mellon University advised by Tuomas Sandholm. His research combines reinforcement learning and game theory to develop AI systems capable of strategic reasoning in imperfect-information multi-agent interactions. He has applied this research to creating Libratus, the first AI to defeat top humans in no-limit poker, which was published in Science and was one of 12 finalists for Science Magazine's Scientific Breakthrough of the Year. Noam received a NIPS Best Paper award in 2017 and an Allen Newell Award for Research Excellence. Prior to starting a PhD, Noam worked at the Federal Reserve researching the effects of algorithm trading on financial markets. Before that, he developed algorithmic trading strategies commercially.