Corpus ID: 2529037

Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization

  title={Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization},
  author={Michael Bradley Johanson and N. Bard and Marc Lanctot and R. Gibson and Michael Bowling},
Recently, there has been considerable progress towards algorithms for approximating Nash equilibrium strategies in extensive games. [...] Key Method By sampling only the public chance outcomes seen by all players, we take advantage of the imperfect information structure of the game to (i) avoid recomputation of strategy probabilities, and (ii) achieve an algorithmic speed improvement, performing O(n2) work at terminal nodes in O(n) time. We demonstrate that this new CFR update converges more quickly than chance…Expand
Search in Imperfect Information Games Using Online Monte Carlo Counterfactual Regret Minimization
Using Regret Estimation to Solve Games Compactly
Regret Minimization in Non-Zero-Sum Games with Applications to Building Champion Multiplayer Computer Poker Agents
Solving Imperfect Information Games Using Decomposition
Solving Large Imperfect Information Games Using CFR+
Combining No-regret and Q-learning