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Equation 1 · Part 7 · Comparing the Main Approaches to Robotics and Embodied AI

addition

Q(s,a)←Q(s,a)+α[r+γmax⁡a′Q(s′,a′)−Q(s,a)]Q(s,a) \leftarrow Q(s,a) + \alpha \left[ r + \gamma \max_{a'} Q(s',a') - Q(s,a) \right]
addition

What this part means

Add the term after the plus sign to the term or group before it.

Its job in the formula

Add the term after the plus sign to the term or group before it.

The passage around this formula

A model-free update never references a transition model. A canonical form, temporal-difference learning, updates a value estimate purely from sampled transitions: Q(s,a)←Q(s,a)+α[r+γmax⁡a′Q(s′,a′)−Q(s,a)]Q(s,a) \leftarrow Q(s,a) + \alpha \left[ r + \gamma \max_{a'} Q(s',a') - Q(s,a) \right]. Nothing in this update requires knowing or estimating p(s' ∣\mid s, a) ; it only requires having experienced (s, a, r, s') . A model-based approach instead fits an explicit dynamics model p^θ(s′∣s,a)\hat{p}_\theta(s' \mid s, a) — a “world model” — and plans against it directly:

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Learn the underlying idea

Addition combines quantities; subtraction measures the signed difference between them. Parentheses show what is combined before the rest of the expression is evaluated.

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Sources cited in the article section

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