Equation 4 · Comparing the Main Approaches to Robotics and Embodied AI
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Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Symbol hatp_θ
hatp_θ is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
Symbol s
s is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
Symbol a
a is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
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What the article says around this equation
Nothing in this update requires knowing or estimating p(s' s, a) ; it only requires having experienced (s, a, r, s') . A model-based approach instead fits an explicit dynamics model — a “world model” — and plans against it directly:
Sources cited in the article section
- [6] Model-Based Reinforcement Learning: A Survey ↗
- [10] Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control ↗
- [9] Mastering Diverse Domains through World Models ↗
- [3] Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning ↗
These citations give research context. Read each source to check which claims it supports.
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