Self-play RL in a takeover auction model shows optimal due diligence is modest and finite, decreasing with cost and competition, while simple general methods outperform specialized ones in large intractable games.
Deep Counterfactual Regret Minimization
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Pith papers citing it
abstract
Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game. This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Low-rank graphs induce latents that form causal abstractions, with identifiability results and a practical objective enabling unsupervised learning of high-level SCMs from low-level measurements.
citing papers explorer
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How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions
Self-play RL in a takeover auction model shows optimal due diligence is modest and finite, decreasing with cost and competition, while simple general methods outperform specialized ones in large intractable games.
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Unsupervised Causal Abstractions Discovery
Low-rank graphs induce latents that form causal abstractions, with identifiability results and a practical objective enabling unsupervised learning of high-level SCMs from low-level measurements.