Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Episodic Value Iteration for Model-based Meta-Reinforcement
  Learning   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.

8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th