Behavior prescribed by active inference dynamics is approximately Bayes-optimal.

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• Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.876
  Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
• These dynamics are consistent with predictive processing since active inference prescribes dynamics that minimise prediction error.concept0.840
  Connection between active inference neuronal dynamics and predictive processing theory.
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.837
  §3, after non-stationary results.
• If a system attains a general steady-state, it will appear to behave in a Bayes optimal fashion, both in terms of optimal Bayesian design (exploration) and Bayesian decision theory (exploitation).hypothesis0.823
  Corollary 3 in Appendix B derived from steady-state assumptions.
• Active inference dispenses with the Bellman optimality principle and replaces it with a (variational) principle of least action.claim0.821
  §2, expected free energy section.
• Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.820
  Abstract and §1, summarizing a key property.
• "active inference separates the problems of optimizing action and perception by assuming that action fulfills predictions based on inferred states"quote0.806
• There is an implicit behavioral equivalence between Bayesian model-based reinforcement learning and active inference when prior preferences are treated as a reward function.claim0.804
  §3, reward shaping conclusion.