  Adaptive traffic signal control is one key avenue for mitigating the growing
consequences of traffic congestion. Incumbent solutions such as SCOOT and SCATS
require regular and time-consuming calibration, can't optimise well for
multiple road use modalities, and require the manual curation of many
implementation plans. A recent alternative to these approaches are deep
reinforcement learning algorithms, in which an agent learns how to take the
most appropriate action for a given state of the system. This is guided by
neural networks approximating a reward function that provides feedback to the
agent regarding the performance of the actions taken, making it sensitive to
the specific reward function chosen. Several authors have surveyed the reward
functions used in the literature, but attributing outcome differences to reward
function choice across works is problematic as there are many uncontrolled
differences, as well as different outcome metrics. This paper compares the
performance of agents using different reward functions in a simulation of a
junction in Greater Manchester, UK, across various demand profiles, subject to
real world constraints: realistic sensor inputs, controllers, calibrated
demand, intergreen times and stage sequencing. The reward metrics considered
are based on the time spent stopped, lost time, change in lost time, average
speed, queue length, junction throughput and variations of these magnitudes.
The performance of these reward functions is compared in terms of total waiting
time. We find that speed maximisation resulted in the lowest average waiting
times across all demand levels, displaying significantly better performance
than other rewards previously introduced in the literature.
