---
title: "Hindsight Optimization"
source: https://www.jemoka.com/posts/kbhhindsight_optimization/
---

If we are tele-operating a robot, we ideally want to minimize cost. We want to estimate a user&rsquo;s goal via user inputs. Predict the most likely goal + assist for it.
&ldquo;find a cost function for which user input \(u\) is optimal&rdquo;.
system does not know the goal the user may not change their goal on a whim Hindsight Optimization To solve this, we use QMDP: &ldquo;select the most optimal actions to estimating cost-to-go assuming full observability&rdquo;.
\begin{equation} Q(b,a,u) = \sum_{g}^{} b(g) Q_{g}(x,a,u) \end{equation}
Result users felt less in control with Hindsight Optimization, despite reaching the goal faster with this policy.
Challenging the results between &ldquo;task completion&rdquo; vs. &ldquo;user satisfaction&rdquo;.
