What is recorded here?
Used sampled game-tree traversals and neural counterfactual-advantage estimation to approximate linear CFR without a manually specified state abstraction, with a separately learned average strategy.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Originalv1 idealized theorem uses infinite buffers, infinitely many sampled traversals and minimum possible approximation error; finite practical neural training is not thereby certified equilibrium convergence. Function approximation is a learned generalization, not absence of representation assumptions. Linear CFR and external sampling are inherited.
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People and systems
Noam Brown; Adam Lerer; Sam Gross; Tuomas Sandholm
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Deep CFR: learned counterfactual advantage and average strategy
Used sampled game-tree traversals and neural counterfactual-advantage estimation to approximate linear CFR without a manually specified state abstraction, with a separately learned average strategy.
Scientific scope
Originalv1 idealized theorem uses infinite buffers, infinitely many sampled traversals and minimum possible approximation error; finite practical neural training is not thereby certified equilibrium convergence. Function approximation is a learned generalization, not absence of representation assumptions. Linear CFR and external sampling are inherited.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Noam Brown; Adam Lerer; Sam Gross; Tuomas Sandholm
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.