… reinforcement learning (DRL) method to address the continuous-horizon decision-making
problem on the highway. First, the vehicle … The running objective of the ego automated vehicle …

… Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles
… recently suggested Multi-Agent Reinforcement Learning techniques with smart routing for …

… reinforcement learning in simulation to obtain a driving system controlling a full-size real-world
vehicle… be used for training and testing of autonomous vehicles. A deep RL framework for …

… of autonomous vehicles (AVs). … a vehicle-to-everything environment. Based on safe,
comfortable, and efficient speed planning via dynamic programming, a deep reinforcement learning-…

… is to investigate Reinforcement Learning (RL) methods for autonomous vehicle control. More
… ) using only image data and internal states of the vehicle as input. The two RL-models will …

… therefore desirable to design CACC for mixed-autonomy, multi-vehicle system. Examples of
… -driven reinforcement learning (RL) based approach. As the joint area of machine learning …

… process for monitoring of autonomous vehicles' dynamics system, these … reinforcement
learning algorithm (NDRL) that can be used to maximize the robustness of autonomous vehicle …

… 𝐶𝐴𝑉 CAVs are filtered out as the ingredient for the reinforcement learning module. As
stated previously, the reinforcement learning algorithm used in this paper is the DDPG agent. …

… vehicle speed (to stabilize the traffic), this paper presents a multi-agent Deep Reinforcement
Learning (… both Traffic light signals and Connected Autonomous Vehicles (CAV). Therefore, …

… In the validation of autonomous vehicles, it can be … autonomous vehicle with noisy sensors
approaching a pedestrian crosswalk. This paper also proposes deep reinforcement learning (…