Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Driving Style Encoder: Situational Reward Adaptation for General-Purpose
  Planning in Automated Driving   General-purpose planning algorithms for automated driving combine mission,
behavior, and local motion planning. Such planning algorithms map features of
the environment and driving kinematics into complex reward functions. To
achieve this, planning experts often rely on linear reward functions. The
specification and tuning of these reward functions is a tedious process and
requires significant experience. Moreover, a manually designed linear reward
function does not generalize across different driving situations. In this work,
we propose a deep learning approach based on inverse reinforcement learning
that generates situation-dependent reward functions. Our neural network
provides a mapping between features and actions of sampled driving policies of
a model-predictive control-based planner and predicts reward functions for
upcoming planning cycles. In our evaluation, we compare the driving style of
reward functions predicted by our deep network against clustered and linear
reward functions. Our proposed deep learning approach outperforms clustered
linear reward functions and is at par with linear reward functions with
a-priori knowledge about the situation.

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The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys