  Intelligent traffic lights in smart cities can optimally reduce traffic
congestion. In this study, we employ reinforcement learning to train the
control agent of a traffic light on a simulator of urban mobility. As a
difference from existing works, a policy-based deep reinforcement learning
method, Proximal Policy Optimization (PPO), is utilized other than value-based
methods such as Deep Q Network (DQN) and Double DQN (DDQN). At first, the
obtained optimal policy from PPO is compared to those from DQN and DDQN. It is
found that the policy from PPO performs better than the others. Next, instead
of the fixed-interval traffic light phases, we adopt the light phases with
variable time intervals, which result in a better policy to pass the traffic
flow. Then, the effects of environment and action disturbances are studied to
demonstrate the learning-based controller is robust. At last, we consider
unbalanced traffic flows and find that an intelligent traffic light can perform
moderately well for the unbalanced traffic scenarios, although it learns the
optimal policy from the balanced traffic scenarios only.
