  The transition from conventional mobility to electromobility largely depends
on charging infrastructure availability and optimal placement.This paper
examines the optimal placement of charging stations in urban areas. We maximise
the charging infrastructure supply over the area and minimise waiting, travel,
and charging times while setting budget constraints. Moreover, we include the
possibility of charging vehicles at home to obtain a more refined estimation of
the actual charging demand throughout the urban area. We formulate the
Placement of Charging Stations problem as a non-linear integer optimisation
problem that seeks the optimal positions for charging stations and the optimal
number of charging piles of different charging types. We design a novel Deep
Reinforcement Learning approach to solve the charging station placement problem
(PCRL). Extensive experiments on real-world datasets show how the PCRL reduces
the waiting and travel time while increasing the benefit of the charging plan
compared to five baselines. Compared to the existing infrastructure, we can
reduce the waiting time by up to 97% and increase the benefit up to 497%.
