Adaptive Parameterized Control for Coordinated traffic Management Using Reinforcement Learning

Traffic control is essential to reduce congestion in both urban and freeway traffic networks. These control measures include ramp metering and variable speed limits for freeways, and traffic signal control for urban traffic. However, current traffic control methods are either too simple to respond to complex traffic environment, or too...

A Comprehensive Survey of Multiagent Reinforcement Learning

Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity ofmany tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning....

Experience-based model predictive control using reinforcement learning

Model predictive control (MPC) is becoming an increasingly popular method to select actions for controlling dynamic systems. TraditionallyMPC uses a model of the system to be controlled and a performance function to characterize the desired behavior of the system. The MPC agent finds actions over a finite horizon that lead the system into a...