• Computer Science
  • Published in AAMAS 2012

Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization

@inproceedings{Johanson2012EfficientNE,
  title={Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization},
  author={Michael Johanson and Nolan Bard and Marc Lanctot and Richard G. Gibson and Michael Bowling},
  booktitle={AAMAS},
  year={2012}
}
Recently, there has been considerable progress towards algorithms for approximating Nash equilibrium strategies in extensive games. One such algorithm, Counterfactual Regret Minimization (CFR), has proven to be effective in two-player zero-sum poker domains. While the basic algorithm is iterative and performs a full game traversal on each iteration, sampling based approaches are possible. For instance, chance-sampled CFR considers just a single chance outcome per traversal, resulting in faster… CONTINUE READING

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