Dynamic Thresholding and Pruning for Regret Minimization

  title={Dynamic Thresholding and Pruning for Regret Minimization},
  author={Noam Brown and Christian Kroer and Tuomas Sandholm},
Regret minimization is widely used in determining strategies for imperfect-information games and in online learning. In large games, computing the regrets associated with a single iteration can be slow. For this reason, pruning – in which parts of the decision tree are not traversed in every iteration – has emerged as an essential method for speeding up iterations in large games. The ability to prune is a primary reason why the Counterfactual Regret Minimization (CFR) algorithm using regret… CONTINUE READING