Year: 2000
Type: conference-paper
Abstract: This paper describes using multi-agent reinforcement learning (RL) algorithms for learning traffic light controllers to minimize the overall waiting time of cars in a city. The RL systems learn value functions estimating expected waiting times for cars given different settings of traffic lights. Selected settings of traffic lights result from combining the predicted waiting times of all cars involved. We investigate RL systems using different kinds of global communicated information between traffic light agents. We also show how the value functions can be used by the driving policies of cars to select optimal routes to destination addresses. The experimental results show that the RL algorithms can outperform non-adaptable traffic light controllers, and that optimizing driving policies is very useful.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7011621630