Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Reinforcement Learning through Imitation Learning Pretraining
  Towards Image-based Autonomous Driving   We present a training pipeline for the autonomous driving task given the
current camera image and vehicle speed as the input to produce the throttle,
brake, and steering control output. The simulator Airsim's convenient weather
and lighting API provides a sufficient diversity during training which can be
very helpful to increase the trained policy's robustness. In order to not limit
the possible policy's performance, we use a continuous and deterministic
control policy setting. We utilize ResNet-34 as our actor and critic networks
with some slight changes in the fully connected layers. Considering human's
mastery of this task and the high-complexity nature of this task, we first use
imitation learning to mimic the given human policy and leverage the trained
policy and its weights to the reinforcement learning phase for which we use
DDPG. This combination shows a considerable performance boost comparing to both
pure imitation learning and pure DDPG for the autonomous driving task.

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The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an