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      "statement": "# Policy-space response oracles and deep cognitive hierarchies\n\nGrew empirical meta-games of policies by learning approximate best responses to policy mixtures, then applied meta-strategy solvers; a decoupled hierarchy reduced the full payoff-tensor bottleneck.\n\n## Scientific scope\n\nGeneralizes prior double-oracle, empirical game theory and fictitious-play ideas. Approximate response quality, finite policy populations and choice of meta-solver limit guarantees; a meta-game solution is not automatically a full-game Nash equilibrium. Full empirical payoff tensors have combinatorial cost, not free scaling.\n\n## Reading status\n\nThis 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.\n\n## People and systems\n\nMarc Lanctot; Vinicius Zambaldi; Audrūnas Gruslys; Angeliki Lazaridou; Karl Tuyls; Julien Pérolat; David Silver; Thore Graepel\n\nSource-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.\n\n\n\n## Original sources\n\n- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://arxiv.org/pdf/1711.00832v1)\n",
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