Title: Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning,
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.