Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario
Traffic signal control is an emerging application scenario for reinforcement learning. Besides
being as an important problem that affects people's daily life in commuting, traffic signal …
being as an important problem that affects people's daily life in commuting, traffic signal …
Cooperation-aware reinforcement learning for merging in dense traffic
Decision making in dense traffic can be challenging for autonomous vehicles. An
autonomous system only relying on predefined road priorities and considering other drivers …
autonomous system only relying on predefined road priorities and considering other drivers …
[PDF][PDF] Multiagent Reinforcement Learning for Traffic Signal Control: a k-Nearest Neighbors Based Approach.
The increasing demand for mobility in our society poses various challenges to traffic
engineering, computer science in general, and artificial intelligence in particular. As it is …
engineering, computer science in general, and artificial intelligence in particular. As it is …
A Decision-making Approach for Complex Unsignalized Intersection by Deep Reinforcement Learning
S Li, K Peng, F Hui, Z Li, C Wei… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Decision-making for automatic vehicles at unsignalized intersections with dense traffic is
one of the most challenging tasks. Due to the complex structure and frequent traffic …
one of the most challenging tasks. Due to the complex structure and frequent traffic …
Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections
Intersections are essential road infrastructures for traffic in modern metropolises. However,
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence 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 …
Graph convolution-based deep reinforcement learning for multi-agent decision-making in mixed traffic environments
An efficient and reliable multi-agent decision-making system is highly demanded for the safe
and efficient operation of connected autonomous vehicles in intelligent transportation …
and efficient operation of connected autonomous vehicles in intelligent transportation …
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning
L Weiwei, H Wenxuan, J Wei, L Lanxin… - arXiv preprint arXiv …, 2024 - arxiv.org
Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have
shown impressive results in many driving scenarios. However, the performance of these …
shown impressive results in many driving scenarios. However, the performance of these …
A reinforcement learning benchmark for autonomous driving in intersection scenarios
In recent years, control under urban intersection scenarios has become an emerging
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
Sociallight: Distributed cooperation learning towards network-wide traffic signal control
Many recent works have turned to multi-agent reinforcement learning (MARL) for adaptive
traffic signal control to optimize the travel time of vehicles over large urban networks …
traffic signal control to optimize the travel time of vehicles over large urban networks …