Real-Time Road Network Optimization with Coordinated Reinforcement Learning

Road Artery Traffic Light Optimization with Use of Reinforcement Learning

The basic principle of optimal traffic control is the appropriate real-time response to dynamic traffic flow changes. Signal plan efficiency depends on a large number of input parameters. An actuated signal system can adjust very well to traffic conditions, but cannot fully adjust to stochastic traffic volume oscillation. Due to the complexity of the problem analytical methods are not applicabl...

Real-Time Coordinated Signal Control Using Agents with Online Reinforcement Learning

This paper introduces a multi-agent architecture for real-time coordinated signal control in an urban traffic network. The multi-agent architecture consists of three hierarchical layers of controller agents: intersection, zone and regional controllers. Each controller agent is implemented by applying artificial intelligence concepts namely fuzzy logic, neural network and evolutionary algorithm....

Coordinated Reinforcement Learning

We present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms is a parameterized, structured representation of a policy or value function. This structure is leveraged in an approach we call coordinated reinforcement learning, by which agents coordinate both their action selection activities and their parameter updates. Within the limits of our par...

Real-Time Reinforcement Learning in Continuous Domains

Complexity Analysis of Real-Time Reinforcement Learning

Real-Time Reinforcement Learning Sven Koenig and Reid G. Simmons School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 [email protected], [email protected] Abstract This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of reaching a goal state in d...