The elimination of reward as a motivator of behavior with prior beliefs dissolves the tautology of reinforcement learning (rewards reinforce behaviors that secure rewards).

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Tautology of Reinforcement Learningconcept0.828
  The circular definition in RL where rewards reinforce behaviors that secure rewards, e.g., going to a cafe because coffee is rewarding.
• Reinforcement learning can be regarded as a limiting or special case of model-based approaches in general — or active inference in particular — when epistemic value is removed.claim0.808
  §3 Discussion.
• Empowerment as intrinsic reward bridges causal learning and reinforcement learning in agent development.claim0.802
• The results of abductive reasoning (reduced model priors) can be communicated to other agents as prior beliefs, provided all agents share the same model lexicon or hypothesis space.claim0.786
  Explanation of how knowledge (not just parameters) is shared between agents; links to pre-Cartesian consciousness
• Reinforcement learning acting on individual characteristics affecting their connections to others can result in dynamics that are equivalent to unsupervised learning at the system scale.claim0.785
  Key insight linking individual rewards to system-level learning.
• Rewards reinforce behaviors that secure rewards.concept0.779
  Highlights circularity in RL reward hypothesis; grounds motivation for preference-based active inference.
• Reinforcement learning is sufficient for agency.claim0.778
  Argument that RL meets the agency indicator.
• successful agents exhibited causal emergence that was consistently predictive of final reward early in training and whose representational dynamics aligned with reward improvement in most tasks.quote0.776
  Load-bearing summary of the main empirical finding that anchors the Causally Emergent Alignment Hypothesis.