Reinforcement learning is sufficient for agency.

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

• Robust agency suffices for moral patienthood.claim0.803
  Normative premise of the robust agency route.
• 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.claim0.802
  Key insight linking individual rewards to system-level learning.
• The human capacity to recognize and evaluate agency is well-tuned for medium sized objects at medium speeds in 3D space, but not adapted to unfamiliar guises and problem spaces.claim0.801
  Claim about the limits of human intuition for detecting intelligence/sentience.
• 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.claim0.798
  §3 Discussion.
• Raileanu et al. 2018 - Modeling Others Using Oneself in Multi-Agent Reinforcement Learningconcept0.797
  Reference for Self-Other Modeling (SOM) framework, a related but less scalable approach to SOO
• Empowerment as intrinsic reward bridges causal learning and reinforcement learning in agent development.claim0.795
• "reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal"quote0.792
  Operational definition of RL used throughout the paper, quoted from Sutton.
• The correct level of agency for a system is an empirical question determined by which intervention strategy provides the most efficient prediction and control.claim0.789
  Central methodological claim of TAME: optimal position on the persuadability continuum is found through experiments, not philosophical definition.