This article investigates the development of a multi-agent deep reinforcement learning (MADRL) controller tailored for CAVs operating within mixed and dynamic ...
The development of intelligent transportation systems (ITS) has attracted significant attention to connected and autonomous vehicles (CAVs).
They employ a control strategy built on distributed proximal policy optimization (DPPO) to anticipate disturbances and downstream traffic conditions in mixed ...
Feb 25, 2024 · This paper proposes a novel differential variable speed limit control (DVSLC) strategy based on multi-agent reinforcement learning (MARL) in a ...
The DRL agent acts as a supervisor to identify the DAS functions, including lane changes, cruise control, and lane maintenance, to optimize average speed and ...
This paper proposes a cooperative strategy of connected and automated vehicles (CAVs) longitudinal control for a mixed connected and automated traffic ...
The proposed algorithm can be deployed on centralized control infrastructures such as road-side units (RSU) or cloud platforms to improve the CAV operation.
This article investigates the development of a multi-agent deep reinforcement learning (MADRL) controller tailored for CAVs operating within mixed and dynamic ...
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Jul 18, 2022 · Results show remarkable improvements when compared to traffic light control techniques (reducing travel time by 59% or reducing time lost due to ...
Reinforcement learning (RL) is generally used to train an agent's policy to learn to act optimally in its environment. Figure 1 illustrates a conceptual ...