Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pre-training with Non-expert Human Demonstration for Deep Reinforcement
  Learning   Deep reinforcement learning (deep RL) has achieved superior performance in
complex sequential tasks by using deep neural networks as function
approximators to learn directly from raw input images. However, learning
directly from raw images is data inefficient. The agent must learn feature
representation of complex states in addition to learning a policy. As a result,
deep RL typically suffers from slow learning speeds and often requires a
prohibitively large amount of training time and data to reach reasonable
performance, making it inapplicable to real-world settings where data is
expensive. In this work, we improve data efficiency in deep RL by addressing
one of the two learning goals, feature learning. We leverage supervised
learning to pre-train on a small set of non-expert human demonstrations and
empirically evaluate our approach using the asynchronous advantage actor-critic
algorithms (A3C) in the Atari domain. Our results show significant improvements
in learning speed, even when the provided demonstration is noisy and of low
quality.

17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci