Richard Sutton’s four pillars of reinforcement learning, policy, value function, perception, and transition, are elegant in their clarity, yet they also reveal a persistent weakness. Each is learned within the boundaries of a particular environment, and each tends to collapse when those boundaries change. The policy that works in one task rarely works in another, the value function is bound to a predefined reward, the perceptual mapping is tied to a narrow distribution of states, and the transition model is valid only for one set of dynamics. This is the transfer problem, the difficulty of carrying knowledge forward into new situations.
World Mind begins from a different ground. Rather than mapping states to actions or estimating returns against fixed rewards, it is built upon existential structures of disclosure, the layered ways in which beings relate to a world. These structures are not metaphysical abstractions left beyond engineering but can be simulated as recurring patterns of orientation, projection, marking, and revision that reappear wherever intelligence arises. So, if Sutton’s categories are traced through the World Mind perspective, they begin to look very different.
A policy in reinforcement learning is a brittle mapping from state to action. In World Mind it is the orientation of drives toward salience and projected possibilities. Actions arise from tensions that seek resolution and from anticipations that sketch what might be. Because drives are universal rather than domain specific, this orientation carries forward naturally into new environments without requiring remapping.
A value function in reinforcement learning is a calculation of cumulative reward. World Mind does not rely on external signals but evaluates through the modulation of drive tensions, their balance, satisfaction, or frustration. This economy of care is always present wherever drives exist, which means the sense of value does not vanish when the environment changes.
Perception in reinforcement learning is the technical problem of representation, but in World Mind it is disclosure, the revealing of beings in their meaningful relations. Phantasia enriches perception by sketching what could be, while ostension fixes reference and stabilizes what is disclosed. Because disclosure is a structural condition of worldhood, not a dataset, perception carries across contexts without being relearned each time.
The transition function in reinforcement learning is a probability table predicting how actions alter states. In World Mind, transitions are structured by logos and nous, the capacity to synthesize and divide, to discern what follows from what. These are not tied to one domain but are formal operations that generalize across them, projecting causal possibility wherever the agent finds itself.
By reframing Sutton’s categories in this way, World Mind transforms the transfer problem. Policies, values, perceptions, and transitions no longer shatter when the environment changes, because they are grounded in what endures across all environments, namely drives, phantasia, ostension, logos, and nous. What reinforcement learning treats as a fragile mapping becomes in World Mind a natural consequence of being-in-the-world.
Transfer, in this view, is not a trick achieved by scaling or fine tuning but a structural continuity that accompanies intelligence wherever it goes.