Can active inference agents learn their own prior preferences without explicit reward signals?

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• Active inference agents can learn epistemic habits in the absence of extrinsic rewards through ambiguity minimization.claim0.856
  §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.claim0.842
  Abstract; central distinction.
• How does active inference compare to reinforcement learning in environments with no rewards or uninformative prior preferences?question0.833
  Core question addressed by the simulations when rewards are removed.
• There is an implicit behavioral equivalence between Bayesian model-based reinforcement learning and active inference when prior preferences are treated as a reward function.claim0.824
  §3, reward shaping conclusion.
• Active inference agent with learnable preferences developed a strict preference for goals (score +) when the Frisbee location was encountered first, becoming a goal-seeking agent.finding0.815
  Figure 5.4 and text.
• Active inference agents engage in information-seeking behavior in reward-free FrozenLake environments, contrasting with Q-learning but similar to Bayesian RL.finding0.803
  Empirical demonstration on FrozenLake; shows epistemic value drives exploration absent reward signal.
• The natural curiosity emerging in active inference contrasts with handcrafted exploration in reinforcement learning such as epsilon-greedy or ad hoc novelty bonuses.claim0.792
  §2, comparing exploration mechanisms.
• "active inference separates the problems of optimizing action and perception by assuming that action fulfills predictions based on inferred states"quote0.788