Learning Options with Interest Functionsdoi:10.1609/AAAI.V33I01.33019955Khimya KhetarpalDoina PrecupAssociation for the Advancement of Artificial Intelligence (AAAI)National Conference on Artificial Intelligence
Research on the psychology of learning has highlighted straightforward ways of enhancing learning. However, effective learning strategies are underused by learners. In this Review, we discuss key research findings on two specific learning strategies: spa
with bias vectors b(1), b(2); weight matrices W(1), W(2) and activation functions G and s. The set of parameters to learn is the set θ = {W(1), b(1), W(2), b(2)}. Typical choices for s include tanh function with tanh(a) = (ea − e− a)/(ea + e− a) ...
One reason for increased interest in online learning is the convenience, according to some experts. “Today’s students are looking for more flexible options,” Willmott says. “Even some students who land in some of the best colleges and universities in the country, they are still ...
Available language has two options to choose from: All supported languages in Viva Learning - selects all languages supported by Viva Learning. By default, this option is selected. Viva Learning default language chosen above can't be removed. A subset of specific languages - provides an option...
Bruner thought that discovery learning has several functions: (1) increasing intellectual potential, (2) enabling external motivation to become intrinsic motivation, (3) being good at discovery, and (4) helping to maintain memory of the learning materials. Therefore, cognitive discovery is worthy of...
The causally informed acquisition function generally outperforms existing criteria, allowing for optimal intervention design with fewer but carefully selected samples.This is a preview of subscription content, access via your institution Access options...
(DDPG) agent is used. This example does not exactly reproduce the approach from [2] because Caoet.al. recommend a Q-learning approach with two separate Q-functions (one for the hedging cost and one for the expected square of the hedging cost), but this example uses instead a simplified ...
Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. F
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