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Reward design for driver repositioning using multi-agent reinforcement learning
طراحی پاداش برای تغییر مکان راننده با استفاده از یادگیری تقویتی چند عامل-2020 A large portion of passenger requests is reportedly unserviced, partially due to vacant for-hire
drivers’ cruising behavior during the passenger seeking process. This paper aims to model the
multi-driver repositioning task through a mean field multi-agent reinforcement learning (MARL)
approach that captures competition among multiple agents. Because the direct application of
MARL to the multi-driver system under a given reward mechanism will likely yield a suboptimal
equilibrium due to the selfishness of drivers, this study proposes a reward design scheme with
which a more desired equilibrium can be reached. To effectively solve the bilevel optimization
problem with upper level as the reward design and the lower level as a multi-agent system, a
Bayesian optimization (BO) algorithm is adopted to speed up the learning process. We then apply
the bilevel optimization model to two case studies, namely, e-hailing driver repositioning under
service charge and multiclass taxi driver repositioning under NYC congestion pricing. In the first
case study, the model is validated by the agreement between the derived optimal control from BO
and that from an analytical solution. With a simple piecewise linear service charge, the objective
of the e-hailing platform can be increased by 8.4%. In the second case study, an optimal toll
charge of $5.1 is solved using BO, which improves the objective of city planners by 7.9%,
compared to that without any toll charge. Under this optimal toll charge, the number of taxis in
the NYC central business district is decreased, indicating a better traffic condition, without
substantially increasing the crowdedness of the subway system. Keywords: Mean field multi-agent reinforcement learning | Reward design | Bayesian optimization |
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