Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.

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• Active inference agents can learn epistemic habits in the absence of extrinsic rewards through ambiguity minimization.claim0.890
  §2, summarizing information-seeking behavior.
• In active inference, reward can simply be treated as another observation we have a preference over, rather than a special signal.claim0.876
  Abstract; central distinction.
• Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.851
  Abstract and §1, summarizing a key property.
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.847
  §3, after non-stationary results.
• Active inference provides a framework (derived from first principles) for solving and understanding the behavior of autonomous agents.claim0.844
• In brief, active inference proposes that agents achieve this by optimising two complementary objective functions, a variational free energy and an expected free energy.quote0.844
  Concise statement of the core hypothesis from Section 2.
• How does active inference compare to reinforcement learning in environments with no rewards or uninformative prior preferences?question0.841
  Core question addressed by the simulations when rewards are removed.
• Active inference postulates that agents achieve survival by optimising two complementary objective functions, a variational free energy and an expected free energy.claim0.840
  Core claim of active inference stated in Section 2.