---
title: "Reconstructed Slides: Reinforcement Learning for Agents - Will Brown, ML Researcher at Morgan Stanley"
category: "slides"
video_id: "JIsgyk0Paic"
sourceLabels: ["Cropped public YouTube video frames", "Local OpenCV slide-region detection", "Local RapidOCR"]
---

# Reconstructed Slides: Reinforcement Learning for Agents - Will Brown, ML Researcher at Morgan Stanley

## Source Video
[Reinforcement Learning for Agents - Will Brown, ML Researcher at Morgan Stanley](https://www.youtube.com/watch?v=JIsgyk0Paic)

## Method
This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

## Reconstructed Slides
![[assets/reconstructed-slides/JIsgyk0Paic/slide-001.jpg]]

- Source frame: `slide-001.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `179.52`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-002.jpg]]

- Source frame: `slide-002.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `177.21`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-003.jpg]]

- Source frame: `slide-003.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `178.93`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-004.jpg]]

- Source frame: `slide-004.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `165.77`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-005.jpg]]

- Source frame: `slide-005.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `177.12`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-006.jpg]]

- Source frame: `slide-006.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `165.08`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-007.jpg]]

- Source frame: `slide-007.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `165.91`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-008.jpg]]

- Source frame: `slide-008.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `178.22`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-009.jpg]]

- Source frame: `slide-009.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `167.29`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-010.jpg]]

- Source frame: `slide-010.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.86`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-011.jpg]]

- Source frame: `slide-011.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `177.39`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-012.jpg]]

- Source frame: `slide-012.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `170.96`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-013.jpg]]

- Source frame: `slide-013.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.27`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-014.jpg]]

- Source frame: `slide-014.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.35`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-015.jpg]]

- Source frame: `slide-015.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `177.08`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-016.jpg]]

- Source frame: `slide-016.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `178.5`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-017.jpg]]

- Source frame: `slide-017.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `178.56`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-018.jpg]]

- Source frame: `slide-018.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `174.47`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-019.jpg]]

- Source frame: `slide-020.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.35`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-020.jpg]]

- Source frame: `slide-021.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `179.1`
![[assets/reconstructed-slides/JIsgyk0Paic/slide-021.jpg]]

- Source frame: `slide-022.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `179.49`
## Dense Scene-Detected Slide Candidates
- [[youtube-JIsgyk0Paic-dense-slides]]