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imperfect_information [2018/11/18 18:48]
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imperfect_information [2018/12/12 12:33]
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 In this work we introduce a generalized form of Counterfactual Regret Minimization that provably finds optimal strategies under any feasible set of convex constraints. ​ In this work we introduce a generalized form of Counterfactual Regret Minimization that provably finds optimal strategies under any feasible set of convex constraints. ​
  
 +https://​arxiv.org/​abs/​1805.08195 Depth-Limited Solving for Imperfect-Information Games
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 +Depth-Limited Solving for Imperfect-Information Games
 +Noam Brown, Tuomas Sandholm, Brandon Amos
 +(Submitted on 21 May 2018)
 +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.