Network-wide traffic signal control optimization using a multi-agent
  deep reinforcement learning
  Inefficient traffic control may cause numerous problems such as traffic
congestion and energy waste. This paper proposes a novel multi-agent
reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep
Deterministic Policy Gradient) to achieve optimal control by enhancing the
cooperation between traffic signals. By introducing the knowledge-sharing
enabled communication protocol, each agent can access to the collective
representation of the traffic environment collected by all agents. The proposed
method is evaluated through two experiments respectively using synthetic and
real-world datasets. The comparison with state-of-the-art reinforcement
learning-based and conventional transportation methods demonstrate the proposed
KS-DDPG has significant efficiency in controlling large-scale transportation
networks and coping with fluctuations in traffic flow. In addition, the
introduced communication mechanism has also been proven to speed up the
convergence of the model without significantly increasing the computational
burden.
