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In reinforcement learning, all objective functions are not equal
Romain Laroche \& Harm van Seijen
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
Abstract:We study the learnability of value functions. We get the reward back propagation out of the way by fitting directly a deep neural network on the analytically computed optimal value function, given a chosen objective function. We show that some objective functions are easier to train than others by several magnitude orders. We observe in particular the influence of the $\gamma$ parameter and the decomposition of the task into subtasks.
Keywords:reinforcement learning, deep learning
TL;DR:In reinforcement learning, all objective functions are not equal
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