Safe and Nested Subgame Solving for Imperfect-Information Games
This addresses the challenge of efficient strategy computation in complex games like poker, enabling AI to achieve superhuman performance, though it is incremental over existing subgame-solving approaches.
The paper tackles the problem of subgame solving in imperfect-information games, where strategies depend on unreached subgames, by introducing new techniques that outperform prior methods in theory and practice, reduce exploitability, and were key to Libratus defeating top humans in heads-up no-limit Texas hold'em poker.
In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games. Nevertheless, it is possible to first approximate a solution for the whole game and then improve it by solving individual subgames. This is referred to as subgame solving. We introduce subgame-solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation. Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability. These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.