Action understanding and active inference

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  Minimizing expected variational free energy under a discrete-state Markov decision process generative model is sufficient to produce curiosity, epistemic learning, and insight without any additional m

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  Active inference agents operating under expected free energy minimization achieve 98.90 [98.00, 99.79] average score in a non-stationary FrozenLake OpenAI gym environment, compared to 64.39 [60.33, 68

• Active Inference: A Process Theory

  A single variational principle—minimizing variational free energy via gradient descent on a Markov decision process (MDP) generative model—is sufficient to derive neuronal dynamics that reproduce, wit

• A tale of two densities: active inference is enactive inference

  Ramstead, Kirchhoff, and Friston argue that generative models in active inference under the free energy principle (FEP) are control systems—not structural representations—and that this distinction has