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The Cartographer
Analysis and Optimization of Deep Counterfactual Value Networks
July 02, 2018 Β· Declared Dead Β· π Deutsche Jahrestagung fΓΌr KΓΌnstliche Intelligenz
"No code URL or promise found in abstract"
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Authors
Patryk Hopner, Eneldo Loza MencΓa
arXiv ID
1807.00900
Category
cs.AI: Artificial Intelligence
Citations
4
Venue
Deutsche Jahrestagung fΓΌr KΓΌnstliche Intelligenz
Last Checked
4 months ago
Abstract
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.
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