Unverified70% confidenceFactExact time
Counterfactual Regret Minimization (CFR) was proposed by Zinkevich et al. in 2007 and was originally used to solve Nash equilibria in large-scale imperfect information games.
2
Sources
70%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCounterfactual regret minimization (CFR) is an iterative algorithm that approximates a Nash equilibrium by minimizing counterfactual regret and has become the mainstream solution method for games with incomplete information such as Texas Hold'em.79% similarUnverifiedHidden-information games are defined in game theory as extensive-form games with imperfect information, and theoretical equilibrium solution methods such as CFR (Counterfactual Regret Minimization) are practically infeasible to deploy at the scale of Stratego due to computational costs68% similarUnverifiedThe choice of bottleneck dimensionality is essentially a trade-off between information compression rate and reconstruction quality, corresponding to rate-distortion theory; Shannon proved in 1959 that there exists a theoretical minimum coding rate lower bound for a given distortion tolerance.61% similarUnverifiedMCCFR (Monte Carlo CFR) reduces the cost of each iteration by sampling only a subset of game paths per iteration, enabling CFR to scale to larger games.60% similarUnverifiedUnconstrained use of compute may encourage low-efficiency brute-force trial-and-error research.60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/893933API
curl https://kongchang.com/api/v1/knowledge/claims/893933MCP
get_claim(id=893933)