Reward Hypothesis

The claim in RL that any goal can be expressed as maximizing the expected cumulative sum of a scalar reward signal.

Neighborhood — ranked by edge-count

concept

Reinforcement learning (RL)
associated_with
Machine learning paradigm where agents learn to maximize cumulative reward through interaction.

cosine ≥ 0.65 · no typed edge

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

Genesis Hypothesisframework0.790
The conjecture that consciousness does not result from the organized mind but creates and maintains complex models of reality; forms at the beginning of mental development
Capacity Hypothesishypothesis0.786
Bigger models are more likely to converge to a shared representation than smaller models because they can better approximate the global optimum
Counterfactual Hypothesisconcept0.780
Ability to entertain competing hypotheses within one inference engine; proposed hallmark of mindful inference
reward as predictable stimuliconcept0.778
Reinterpretation of rewards as simply predictable (unsurprising) stimuli under the free-energy principle.
Simplicity Bias Hypothesishypothesis0.772
Deep networks are biased toward finding simple fits to data, and this bias increases with model size, driving convergence
Reward Functionconcept0.766
In RL, a scalar signal from the environment that defines the agent's goal; in active inference, reward is just another observation with associated preference.
Universality Hypothesisconcept0.759
The hypothesis that analogous features and circuits reliably form across different neural network models and tasks
I-hypothesisframework0.758
The overarching hypothesis that an I or self-like ground underlies matter and becomes visible in living things.