Parameter sharing reinforcement learning for modeling multi-agent driving behavior in roundabout scenarios
F Konstantinidis, M Sackmann… - 2021 IEEE …, 2021 - ieeexplore.ieee.org
Modeling other drivers' behavior in highly interactive traffic situations, such as roundabouts,
is a challenging task. We address this task using a Multi-Agent Reinforcement Learning …
is a challenging task. We address this task using a Multi-Agent Reinforcement Learning …
Multi-agent driving behavior prediction across different scenarios with self-supervised domain knowledge
How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …
autonomous driving. It is significant to have the ability to predict surrounding road …
Modeling Interaction-Aware Driving Behavior using Graph-Based Representations and Multi-Agent Reinforcement Learning
F Konstantinidis, M Sackmann… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations,
such as roundabouts, poses a significant challenge due to the complex interactions and the …
such as roundabouts, poses a significant challenge due to the complex interactions and the …
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 …
Multi-agent Decision-making at Unsignalized Intersections with Reinforcement Learning from Demonstrations
Intersections are key nodes and also bottlenecks of urban road networks, so improving the
traffic efficiency at intersections is beneficial to improving overall traffic throughput and …
traffic efficiency at intersections is beneficial to improving overall traffic throughput and …
Reinforcement learning with iterative reasoning for merging in dense traffic
Maneuvering in dense traffic is a challenging task for autonomous vehicles because it
requires reasoning about the stochastic behaviors of many other participants. In addition, the …
requires reasoning about the stochastic behaviors of many other participants. In addition, the …
Parameter sharing reinforcement learning architecture for multi agent driving
Multi-agent learning provides a potential solution for frameworks to learn and simulate traffic
behaviors. This paper proposes a novel architecture to learn multiple driving behaviors in a …
behaviors. This paper proposes a novel architecture to learn multiple driving behaviors in a …
Multi-agent reinforcement learning for autonomous vehicles: A survey
In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
Microscopic traffic simulation by cooperative multi-agent deep reinforcement learning
G Bacchiani, D Molinari, M Patander - arXiv preprint arXiv:1903.01365, 2019 - arxiv.org
Expert human drivers perform actions relying on traffic laws and their previous experience.
While traffic laws are easily embedded into an artificial brain, modeling human complex …
While traffic laws are easily embedded into an artificial brain, modeling human complex …
Social learning in Markov games: Empowering autonomous driving
In a multi-agent system (MAS), a social learning scheme allows independent agents to learn
through interactions with agents randomly selected from a pool. Such a scheme is important …
through interactions with agents randomly selected from a pool. Such a scheme is important …