… We plan to demonstrate CityFlow in different traffic scenarios and show its capability to … ,
multi-agent reinforcement learning environment for large scale city traffic scenario. Researchers …

… realistic scenario with multiple signalized intersections, this study considers a scenario of …
with different traffic saturation conditions, we run the Cottbus scenario with different capacity …

… traffic signal control for different traffic scenarios. The proposed multi-agent reinforcement
learning (… delay and speed in comparison to other traffic control system like hierarchical multi-…

… We propose a toolkit for developing and comparing reinforcement learning (RL)based traffic
… control problems that are based on realistic traffic scenarios. Importantly, the toolkit allows a …

… We generate 20 policies for each traffic scenario and we run 5000 learning episodes. For
each policy, the number of vehicle hours is computed and the results are shown in Fig. 8. …

… In this paper, we study how to decide the traffic signal … learning model to control the traffic
light cycle. In the model, we quantify the complex traffic scenario as states by collecting traffic …

… , which may reduce total traffic efficiency and the occurrence of traffic accidents. Graph …
traffic scenarios. Therefore, some researchers focused on the graph reinforcement learning (GRL) …

… To promote similar advances in traffic control via RL, we propose four … traffic scenarios,
illustrating distinct reinforcement learning problems with applications to mixed-autonomy traffic. …

… various safe reinforcement learning and multi-agent reinforcement learning algorithms in …
In five typical traffic scenarios such as roundabout and intersection, we study the problem of …

… traffic participants in dense merging scenarios. We show that deep reinforcement learning
policies can capture interaction patterns when trained in a variety of different scenarios, even …