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SULFR: Simulation of Urban Logistic For Reinforcement
Abstract : In urban logistics, various sources of uncertainty can invalidate pre-planned routes. In this context,
a routing strategy that uses available information from the environment could help improve
the overall performance of the routing process by dynamically choosing the next client at the
online execution time. While static and deterministic testbeds for vehicle routing exist, their
stochastic and dynamic counterparts are still missing. This paper proposes an interface to the microtraffic
simulation package SUMO that implement a generative model of stochastic and dynamic
vehicle routing problems. We formalize the latter using a reinforcement learning framework for
semi-Markov decision processes. The resulting testbeds make it possible to compare single- and
multi-agent reinforcement learning algorithms in customizable routing environments. We report
our preliminary tests to evaluate a hand-crafted policy on some basic scenarios.
Cited literature [21 references]
https://hal.inria.fr/hal-01847773
Contributor : Guillaume Bono <>
Submitted on : Friday, September 7, 2018 - 1:21:33 PM
Last modification on : Friday, December 18, 2020 - 9:54:02 AM
Contributor : Guillaume Bono <>
Submitted on : Friday, September 7, 2018 - 1:21:33 PM
Last modification on : Friday, December 18, 2020 - 9:54:02 AM
File
gbono_pgmrl18.pdf
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