Active inference dispenses with the Bellman optimality principle and replaces it with a (variational) principle of least action.

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• Active inference has no random or stochastic aspects; everything changes to minimize variational free energy in accord with Hamilton's principle of least action.claim0.857
  Ontological claim about the deterministic nature of active inference agents in these simulations
• Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents.quote0.841
• In brief, active inference proposes that agents achieve this by optimising two complementary objective functions, a variational free energy and an expected free energy.quote0.838
  Concise statement of the core hypothesis from Section 2.
• Active inference provides a principled account of epistemic exploration and intrinsic motivation as minimizing uncertainty.claim0.837
  §1, listing contributions.
• "active inference separates the problems of optimizing action and perception by assuming that action fulfills predictions based on inferred states"quote0.836
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.834
  §3, after non-stationary results.
• Active inference treats exploration and exploitation as two different ways of tackling the same problem: minimizing uncertainty.claim0.828
  §2, comment on expected free energy decomposition.
• Active inference postulates that agents achieve survival by optimising two complementary objective functions, a variational free energy and an expected free energy.claim0.826
  Core claim of active inference stated in Section 2.