Active Inference — The Learn Arc, Part 47: Session §10.1 — Perception, action, learning
This article explains how perception, action, and learning in Active Inference are not separate algorithms, but rather three gradients of the same free energy function acting on different variables.
Why it matters
This unified view of perception, action, and learning in Active Inference is crucial for understanding the broader framework and its applications.
Key Points
- 1Perception, action, and learning are three gradients of the same free energy function
- 2Perception updates beliefs, action changes the world, and learning updates parameters
- 3These three gradients operate on different timescales and variables, but use the same equation
- 4Precision determines which gradient dominates - high sensory precision favors perception/action, low parameter precision favors learning
- 5This unified view of the framework makes the rest of Active Inference more legible
Details
The article explains that Chapter 10 of the 'Learn Arc' series on Active Inference synthesizes the concepts introduced earlier. It shows that perception, action, and learning are not separate algorithms, but rather three gradients of the same free energy function (F) acting on different variables. Perception updates the belief (∂F/∂μ), action changes the world (∂F/∂a), and learning updates the parameters (∂F/∂θ). These three gradients operate on different timescales, but use the same underlying equation. The relative precision of the sensory and parameter estimates determines which gradient dominates - high sensory precision favors perception and action, while low parameter precision allows learning to run faster. Viewing perception, action, and learning as three handles on the same 'F' equation is what makes the rest of the Active Inference framework more legible and understandable.
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