Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Automated lane change strategy using proximal policy optimization-based deep reinforcement learning
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan,
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
Advances in youla-kucera parametrization: A review
Youla-Kucera (YK) parametrization was formulated decades ago for obtaining the set of
controllers stabilizing a linear plant. This fundamental result of control theory has been used …
controllers stabilizing a linear plant. This fundamental result of control theory has been used …
Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving
The design of high-level decision-making systems is a topical problem in the field of
autonomous driving. In this paper, we combine traditional rule-based strategies and …
autonomous driving. In this paper, we combine traditional rule-based strategies and …
Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles
Using deep reinforcement learning, we successfully train a set of two autonomous vehicles
to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to …
to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to …
Mobility vla: Multimodal instruction navigation with long-context vlms and topological graphs
An elusive goal in navigation research is to build an intelligent agent that can understand
multimodal instructions including natural language and image, and perform useful …
multimodal instructions including natural language and image, and perform useful …
Hierarchical program-triggered reinforcement learning agents for automated driving
B Gangopadhyay, H Soora… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have
demonstrated impressive performance in complex tasks, including autonomous driving. The …
demonstrated impressive performance in complex tasks, including autonomous driving. The …
Coordinated control of urban expressway integrating adjacent signalized intersections using adversarial network based reinforcement learning method
This paper proposes an adversarial reinforcement learning (RL)-based traffic control
strategy to improve the traffic efficiency of an integrated network with expressway and …
strategy to improve the traffic efficiency of an integrated network with expressway and …
Object detection with deep neural networks for reinforcement learning in the task of autonomous vehicles path planning at the intersection
Among a number of problems in the behavior planning of an unmanned vehicle the central
one is movement in difficult areas. In particular, such areas are intersections at which direct …
one is movement in difficult areas. In particular, such areas are intersections at which direct …
Model-based transfer reinforcement learning based on graphical model representations
Reinforcement learning (RL) plays an essential role in the field of artificial intelligence but
suffers from data inefficiency and model-shift issues. One possible solution to deal with such …
suffers from data inefficiency and model-shift issues. One possible solution to deal with such …