Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning

Steven Stenberg Hansen

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arXiv · 1705.03562

Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning

Steven Stenberg Hansen

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Abstract

We present a new deep meta reinforcement learner, which we call Deep Episodic Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity metric for a non-parametric model-based reinforcement learning algorithm. Our model is trained end-to-end via back-propagation. Despite being trained using the model-free Q-learning objective, we show that DEVI's model-based internal structure provides `one-shot' transfer to changes in reward and transition structure, even for tasks with very high-dimensional state spaces.

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BibTeX RIS

Steven Stenberg Hansen. 2017-05-09. Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning. https://arxiv.org/abs/1705.03562

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