## In reinforcement learning, all objective functions are not equal

Feb 12, 2018 (modified: Jun 04, 2018) ICLR 2018 Workshop Submission readers: everyone Show 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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