Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DROP: Deep relocating option policy for optimal ride-hailing vehicle
  repositioning   In a ride-hailing system, an optimal relocation of vacant vehicles can
significantly reduce fleet idling time and balance the supply-demand
distribution, enhancing system efficiency and promoting driver satisfaction and
retention. Model-free deep reinforcement learning (DRL) has been shown to
dynamically learn the relocating policy by actively interacting with the
intrinsic dynamics in large-scale ride-hailing systems. However, the issues of
sparse reward signals and unbalanced demand and supply distribution place
critical barriers in developing effective DRL models. Conventional exploration
strategy (e.g., the $\epsilon$-greedy) may barely work under such an
environment because of dithering in low-demand regions distant from
high-revenue regions. This study proposes the deep relocating option policy
(DROP) that supervises vehicle agents to escape from oversupply areas and
effectively relocate to potentially underserved areas. We propose to learn the
Laplacian embedding of a time-expanded relocation graph, as an approximation
representation of the system relocation policy. The embedding generates
task-agnostic signals, which in combination with task-dependent signals,
constitute the pseudo-reward function for generating DROPs. We present a
hierarchical learning framework that trains a high-level relocation policy and
a set of low-level DROPs. The effectiveness of our approach is demonstrated
using a custom-built high-fidelity simulator with real-world trip record data.
We report that DROP significantly improves baseline models with 15.7% more
hourly revenue and can effectively resolve the dithering issue in low-demand
areas.

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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: supr-con