Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
[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 …
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 …
[HTML][HTML] Fuzzy logic and deep Q learning based control for traffic lights
I Tunc, MT Soylemez - Alexandria Engineering Journal, 2023 - Elsevier
Traffic congestion is a major concern for many metropolises. Although it is difficult to regulate
traffic flow because of numerous complexities and uncertainties, the traffic congestion …
traffic flow because of numerous complexities and uncertainties, the traffic congestion …
A review of reinforcement learning applications in adaptive traffic signal control
In urban areas, the problem of recurring daily congestion is constantly increasing. A possible
solution is seen in the application of adaptive traffic signal control (ATSC) systems for the …
solution is seen in the application of adaptive traffic signal control (ATSC) systems for the …
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
Numerous methods are proposed for the Traffic Signal Control (TSC) tasks aiming to provide
efficient transportation and mitigate congestion waste. In recent, promising results have …
efficient transportation and mitigate congestion waste. In recent, promising results have …
EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as
medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch …
medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch …
[PDF][PDF] Llm powered sim-to-real transfer for traffic signal control
Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to
provide efficient transportation and mitigate congestion waste. Recently, promising results …
provide efficient transportation and mitigate congestion waste. Recently, promising results …
Managing mixed traffic at signalized intersections: An adaptive signal control and CAV coordination system based on deep reinforcement learning
Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and
human-driven vehicles (HVs) at a signalized intersection has become a concern of …
human-driven vehicles (HVs) at a signalized intersection has become a concern of …
Navigating the landscape of deep reinforcement learning for power system stability control: A review
The widespread penetration of inverter-based resources has profoundly impacted the
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …