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Class 1 typo: y -> u #24

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andrewrosemberg merged 1 commit into from
Aug 22, 2025
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Class 1 typo: y -> u #24

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6 changes: 3 additions & 3 deletions class01/class01_intro.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ In this course, we are interested in problems with the following structure:
\phantom{\substack{(\mathbf u_1,\mathbf x_1)\\\mathrm{s.t.}}}%
\!\!\!\!\!\!\!\!\!\!(\mathbf u_1,\mathbf x_1)\in\mathcal X_1(\mathbf x_0)%
}{%
\!\!\!\!c(\mathbf x_1,\mathbf y_1)%
\!\!\!\!c(\mathbf x_1,\mathbf u_1)%
}
+\mathbb{E}_1\Bigl[
\quad \cdots
Expand Down Expand Up @@ -123,7 +123,7 @@ constraints can be generally posed as:
&\mathcal{X}_t(\mathbf{x}_{t-1}, w_t)=
\begin{cases}
f(\mathbf{x}_{t-1}, w_t, \mathbf{u}_t) = \mathbf{x}_t \\
h(\mathbf{x}_t, \mathbf{y}_t) \geq 0
h(\mathbf{x}_t, \mathbf{u}_t) \geq 0
\end{cases}
\end{align}
```
Expand All @@ -135,7 +135,7 @@ where the outgoing state of the system $\mathbf{x}_t$ is a
transformation based on the incoming state, the realized uncertainty,
and the control variables. In the Markov Decision Process (MDP) framework, we refer to $f$ as the "transition kernel" of the system. State and
control variables are restricted further by additional constraints
captured by $h(\mathbf{x}_t, \mathbf{y}_t) \geq 0$. We
captured by $h(\mathbf{x}_t, \mathbf{u}_t) \geq 0$. We
consider policies that map the past information into decisions: $\pi_t : (\mathbf{x}_{t-1}, w_t) \rightarrow \mathbf{x}_t$. In
period $t$, an optimal policy is given by the solution of the dynamic
equations:
Expand Down