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.

See the text for more details of the task in the continuous setting. B: ADP significantly outperforms INC when the task is difficult (low ε). Barplot means are estimated from 10 repeats. C,D: The q values plotted as in <a href="http://www.plosco...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	William L. Tong (22238845) (author)
مؤلفون آخرون:	Venkatesh N. Murthy (15354261) (author), Gautam Reddy (11927277) (author)
منشور في:	2025
الموضوعات:	Neuroscience Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> dogs starting point towards monte carlo planning student &# 8217 sequence learning task previously assigned tasks perform complex tasks general computational framework adaptive shaping heuristic student framework shaping behavior navigation tasks harder tasks adaptive algorithms work provides using algorithms task based minimal parameters laboratory mice involve sparse delayed rewards continuous curricula commonly trained careful balance adaptively alternate
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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.

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