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Analysis and Optimization of Deep Counterfactual Value Networks
Recently a strong poker-playing algorithm called DeepStack was published,
which is able to find an approximate Nash equilibrium during gameplay by using
heuristic values of future states predicted by deep neural networks. This paper
analyzes new ways of encoding the inputs and outputs of DeepStack's deep
counterfactual value networks based on traditional abstraction techniques, as
well as an unabstracted encoding, which was able to increase the network's
accuracy.Comment: Long version of publication appearing at KI 2018: The 41st German
Conference on Artificial Intelligence
(http://dx.doi.org/10.1007/978-3-030-00111-7_26). Corrected typo in titl
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