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A linear mixture MDP is a class of function approximation settings in which the transition probability is assumed to be a linear combination of known basis features: P(s'|s,a)=Σφ_i(s'|s,a)θ_i, where φ_i are known features and θ are unknown parameters, allowing the state space to be huge or even continuous while retaining a learnable linear structure.
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10/12/2026
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