Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ZPD Teaching Strategies for Deep Reinforcement Learning from
  Demonstrations   Learning from demonstrations is a popular tool for accelerating and reducing
the exploration requirements of reinforcement learning. When providing expert
demonstrations to human students, we know that the demonstrations must fall
within a particular range of difficulties called the "Zone of Proximal
Development (ZPD)". If they are too easy the student learns nothing, but if
they are too difficult the student is unable to follow along. This raises the
question: Given a set of potential demonstrators, which among them is best
suited for teaching any particular learner? Prior work, such as the popular
Deep Q-learning from Demonstrations (DQfD) algorithm has generally focused on
single demonstrators. In this work we consider the problem of choosing among
multiple demonstrators of varying skill levels. Our results align with
intuition from human learners: it is not always the best policy to draw
demonstrations from the best performing demonstrator (in terms of reward). We
show that careful selection of teaching strategies can result in sample
efficiency gains in the learner's environment across nine Atari games

7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: ao-sci