arXiv Artificial Intelligence

Active Inference as a Convex Markov Decision Process

2 hours ago 23.09.2026 07:00 👁 1 views
Active Inference as a Convex Markov Decision Process

Quick summary

arXiv:2607.20152v2 Announce Type: replace-cross Abstract: Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principle. We frame AIF as policy optimization and show that, for closed-loop control policies, EFE minimization can be formulated as a convex Markov decision process (MDP). This perspective reveals that policy-dependent reward prediction errors transmit natural gradients of the expected free energy backwards in time rather than up a hierarchy. Finally, we show that c

Key takeaways

  • arXiv:2607.20152v2 Announce Type: replace-cross Abstract: Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principle.
  • We frame AIF as policy optimization and show that, for closed-loop control policies, EFE minimization can be formulated as a convex Markov decision process (MDP).
  • This perspective reveals that policy-dependent reward prediction errors transmit natural gradients of the expected free energy backwards in time rather than up a hierarchy.

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “Active Inference as a Convex Markov Decision Process” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗