Cooperative Traffic Scheduling in Transportation Network: A Knowledge Transfer Method

Deep reinforcement learning (DRL) has shown significant potential in adaptive traffic signal control (ATSC) by adapting to real-time traffic conditions. However, controlling multiple intersections faces challenges, mainly due to the isolated actions of agents and non-stationary caused by other intersections. To address these issues, this paper proposes a novel knowledge collaboration-based actor-critic policy gradient (KCACPG) method to achieve cooperative traffic scheduling across multiple intersections. KCACPG includes a knowledge collaboration learning mechanism that allows heterogeneous agents to exchange knowledge across experience tuples, achieving globally optimal decision-making and coordination. KCACPG also integrates an off-policy prioritized experience replay mechanism to improve knowledge reuse efficiency and reduce the negative impact of knowledge transfer. Simulation results show that KCACPG converges quickly, generalizes to fluctuant traffic and load well, improves the network throughput by up to 17.8%, and reduces the pressure imbalance by up to 11.6% compared with the existing collaborative methods. The proposed method has significant implications for intelligent transportation systems and smart cities.