Multi-agent reinforcement learning for integrated and networked adaptive traffic signal control

摘要

A system and method of multi-agent reinforcement learning for integrated and networked adaptive traffic controllers (MARLIN-ATC). Agents linked to traffic signals generate control actions for an optimal control policy based on traffic conditions at the intersection and one or more other intersections. The agent provides a control action considering the control policy for the intersection and one or more neighboring intersections. Due to the cascading effect of the system, each agent implicitly considers the whole traffic environment, which results in an overall optimized control policy.