Assessing Generalization in TD methods for Deep Reinforcement LearningDownload PDF

Sep 25, 2019 (edited Dec 24, 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • TL;DR: Empirical investigation showing TD, in particular TD(0), may be preventing generalization in DeepRL
  • Abstract: Current Deep Reinforcement Learning (DRL) methods can exhibit both data inefficiency and brittleness, which seem to indicate that they generalize poorly. In this work, we experimentally analyze this issue through the lens of memorization, and show that it can be observed directly during training. More precisely, we find that Deep Neural Networks (DNNs) trained with supervised tasks on trajectories capture temporal structure well, but DNNs trained with TD(0) methods struggle to do so, while using TD(lambda) targets leads to better generalization.
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  • Keywords: reinforcement learning, deep learning, generalization
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