A: The sequence learning setup.
<p>In the full task, the student is required to take a sequence of <i>N</i> correct actions to get reward. In intermediate levels of the task, the reward is delivered if the student takes correct actions. is the innate bias of the student to take the correct action at the <i>...
Saved in:
| Main Author: | William L. Tong (22238845) (author) |
|---|---|
| Other Authors: | Venkatesh N. Murthy (15354261) (author), Gautam Reddy (11927277) (author) |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Algorithms for designing continuous curricula A: Decision tree showing the continuous version of ADP which includes actions that “grow” and “shrink” the increments between continuously parameterized difficulty levels.
by: William L. Tong (22238845)
Published: (2025) -
Deep reinforcement learning agents trained using a curriculum solve navigation tasks with delayed rewards.
by: William L. Tong (22238845)
Published: (2025) -
A: An overview of the POMCP teacher, which cycles between inferring the student’s <i>q</i> values, innate bias and learning rate based on the transcript and planning using a Monte Carlo tree search.
by: William L. Tong (22238845)
Published: (2025) -
A: Teaching using our OCL framework can be visualized using a difficulty landscape (here, parameterized by two skill axes), which quantifies the student’s success probability for each difficulty level.
by: William L. Tong (22238845)
Published: (2025) -
SHQ predicts real-world wayfinding performance in older participants for medium difficulty levels.
by: Sarah Goodroe (18024797)
Published: (2025)