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 …
Traffic3d: A rich 3d-traffic environment to train intelligent agents
The last few years marked a substantial development in the domain of Deep Reinforcement
Learning. However, a crucial and not yet fully achieved objective is to devise intelligent …
Learning. However, a crucial and not yet fully achieved objective is to devise intelligent …
Traffic3d: A new traffic simulation paradigm
The field of Deep Reinforcement Learning has evolved significantly over the last few years.
However, an important and not yet fully-attained goal is to produce intelligent agents which …
However, an important and not yet fully-attained goal is to produce intelligent agents which …
Driver modeling through deep reinforcement learning and behavioral game theory
In this work, a synergistic combination of deep reinforcement learning and hierarchical game
theory is proposed as a modeling framework for behavioral predictions of drivers in highway …
theory is proposed as a modeling framework for behavioral predictions of drivers in highway …
Behaviorally diverse traffic simulation via reinforcement learning
S Shiroshita, S Maruyama, D Nishiyama… - 2020 IEEE/RSJ …, 2020 - ieeexplore.ieee.org
Traffic simulators are important tools in autonomous driving development. While continuous
progress has been made to provide developers more options for modeling various traffic …
progress has been made to provide developers more options for modeling various traffic …
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 …
Deep reinforcement learning for human-like driving policies in collision avoidance tasks of self-driving cars
R Emuna, A Borowsky, A Biess - arXiv preprint arXiv:2006.04218, 2020 - arxiv.org
The technological and scientific challenges involved in the development of autonomous
vehicles (AVs) are currently of primary interest for many automobile companies and …
vehicles (AVs) are currently of primary interest for many automobile companies and …
Vehicle control in highway traffic by using reinforcement learning and microscopic traffic simulation
The paper presents a simple yet powerful and intelligent driver agent, designed to operate in
a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The …
a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The …
Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways
NTT Le - Journal of Information and Telecommunication, 2023 - Taylor & Francis
In the last decade, agent-based modelling and simulation has emerged as a potential
approach to study complex systems in the real world, such as traffic congestion. Complex …
approach to study complex systems in the real world, such as traffic congestion. Complex …
Data-driven Traffic Simulation: A Comprehensive Review
Autonomous vehicles (AVs) have the potential to significantly revolutionize society by
providing a secure and efficient mode of transportation. Recent years have witnessed …
providing a secure and efficient mode of transportation. Recent years have witnessed …
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