[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
[HTML][HTML] 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 …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
Deep reinforcement learning in the advanced cybersecurity threat detection and protection
The cybersecurity threat landscape has lately become overly complex. Threat actors
leverage weaknesses in the network and endpoint security in a very coordinated manner to …
leverage weaknesses in the network and endpoint security in a very coordinated manner to …
A research and educational robotic testbed for real-time control of emerging mobility systems: From theory to scaled experiments [applications of control]
Emerging mobility systems, for example, connected and automated vehicles (CAVs), shared
mobility, and electric vehicles, mark a paradigm shift in which myriad opportunities exist for …
mobility, and electric vehicles, mark a paradigm shift in which myriad opportunities exist for …
Separation of learning and control for cyber–physical systems
AA Malikopoulos - Automatica, 2023 - Elsevier
Most cyber–physical systems (CPS) encounter a large volume of data which is added to the
system gradually in real time and not altogether in advance. In this paper, we provide a …
system gradually in real time and not altogether in advance. In this paper, we provide a …
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 …
[HTML][HTML] Hierarchical motion control strategies for handling interactions of automated vehicles
The paper proposes motion control strategies for automated road vehicles to handle
interactions among vehicles. The control strategies are built in a hierarchical structure, which …
interactions among vehicles. The control strategies are built in a hierarchical structure, which …
Evaluating the robustness of collaborative agents
In order for agents trained by deep reinforcement learning to work alongside humans in
realistic settings, we will need to ensure that the agents are\emph {robust}. Since the real …
realistic settings, we will need to ensure that the agents are\emph {robust}. Since the real …