A survey on model-based reinforcement learning
Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
A review of cooperative multi-agent deep reinforcement learning
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
Transportation 5.0: The DAO to safe, secure, and sustainable intelligent transportation systems
In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee
on Transportation 5.0 with the mission of promoting and transforming the deployment of …
on Transportation 5.0 with the mission of promoting and transforming the deployment of …
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
[PDF][PDF] Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Traffic congestion plagues cities around the world. Recent years have witnessed an
unprecedented trend in applying reinforcement learning for traffic signal control. However …
unprecedented trend in applying reinforcement learning for traffic signal control. However …
Scenarionet: Open-source platform for large-scale traffic scenario simulation and modeling
Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially
accelerate autonomous driving research, especially for perception tasks such as 3D …
accelerate autonomous driving research, especially for perception tasks such as 3D …
Presslight: Learning max pressure control to coordinate traffic signals in arterial network
Traffic signal control is essential for transportation efficiency in road networks. It has been a
challenging problem because of the complexity in traffic dynamics. Conventional …
challenging problem because of the complexity in traffic dynamics. Conventional …
FusionPlanner: A multi-task motion planner for mining trucks via multi-sensor fusion
In recent years, significant achievements have been made in motion planning for intelligent
vehicles. However, as a typical unstructured environment, open-pit mining attracts limited …
vehicles. However, as a typical unstructured environment, open-pit mining attracts limited …
Colight: Learning network-level cooperation for traffic signal control
Cooperation among the traffic signals enables vehicles to move through intersections more
quickly. Conventional transportation approaches implement cooperation by pre-calculating …
quickly. Conventional transportation approaches implement cooperation by pre-calculating …
Smarts: An open-source scalable multi-agent rl training school for autonomous driving
Interaction is fundamental in autonomous driving (AD). Despite more than a decade of
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …