$$a_t \sim \pi_\theta(a \mid o_{\le t},\ h_t,\ g)$$

Equation 1 · AI Agents & Systems

Comparing the Main Approaches to AI Agent Architecture

This equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions.

Strip away the branding and every one of these systems is choosing an action from a history of observations rather than from the true state of the world, because the true state is never directly available to it. That is the founding move of decision theory under partial observability: an agent’s policy must be a function of a belief built from what it has seen, not of the world as it actually is, and the two can silently diverge [ 11 ] . Write the general shape once, for a single decision-maker at a single step: $a_t \sim \pi_\theta(a \mid o_{\le t},\ h_t,\ g)$. where $o_{\le t}$ is the observation history, $h_t$ is whatever state the system has chosen to retain, and g is the task goal. Nothing here is specific to language…

Meanings in this article

• $o_{\le t}$: the observation history.
• $h_t$: whatever state the system has chosen to retain.
• $g$: the task goal.

Equation guide → · Article →