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@jemoka / Jemoka Knowledge Base / wiki/concepts/generative_model.md
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--- title: "generative model" type: concept source: https://www.jemoka.com/posts/kbhgenerative_model/ confidence: high status: active --- Its like a transforming distributions procedure, but your \(f\) is not constrained to be differentiable. So you can still sample from it. we perform a random sample of possible next state (weighted by the action you took, meaning an instantiation of \(s’ \sim T(\cdot | s,a)\)) and reward \(R(s,a)\) from current state