Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample Efficient Interactive End-to-End Deep Learning for Self-Driving
  Cars with Selective Multi-Class Safe Dataset Aggregation   The objective of this paper is to develop a sample efficient end-to-end deep
learning method for self-driving cars, where we attempt to increase the value
of the information extracted from samples, through careful analysis obtained
from each call to expert driver\'s policy. End-to-end imitation learning is a
popular method for computing self-driving car policies. The standard approach
relies on collecting pairs of inputs (camera images) and outputs (steering
angle, etc.) from an expert policy and fitting a deep neural network to this
data to learn the driving policy. Although this approach had some successful
demonstrations in the past, learning a good policy might require a lot of
samples from the expert driver, which might be resource-consuming. In this
work, we develop a novel framework based on the Safe Dateset Aggregation (safe
DAgger) approach, where the current learned policy is automatically segmented
into different trajectory classes, and the algorithm identifies trajectory
segments or classes with the weak performance at each step. Once the trajectory
segments with weak performance identified, the sampling algorithm focuses on
calling the expert policy only on these segments, which improves the
convergence rate. The presented simulation results show that the proposed
approach can yield significantly better performance compared to the standard
Safe DAgger algorithm while using the same amount of samples from the expert.

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The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas