Depth-Limited Solving for Imperfect-Information Games
This addresses a fundamental challenge in game AI for imperfect-information games like poker, enabling more efficient and powerful agents with limited computational resources.
The paper tackled the problem of depth-limited search in imperfect-information games by introducing a method that allows opponents to choose strategies at the depth limit, forcing robustness to different strategies. It demonstrated effectiveness by building a master-level poker AI that defeats prior top agents using only a 4-core CPU and 16 GB of memory, whereas previously a supercomputer was required.
A fundamental challenge in imperfect-information games is that states do not have well-defined values. As a result, depth-limited search algorithms used in single-agent settings and perfect-information games do not apply. This paper introduces a principled way to conduct depth-limited solving in imperfect-information games by allowing the opponent to choose among a number of strategies for the remainder of the game at the depth limit. Each one of these strategies results in a different set of values for leaf nodes. This forces an agent to be robust to the different strategies an opponent may employ. We demonstrate the effectiveness of this approach by building a master-level heads-up no-limit Texas hold'em poker AI that defeats two prior top agents using only a 4-core CPU and 16 GB of memory. Developing such a powerful agent would have previously required a supercomputer.