… in mixed-autonomy traffic. In this study, we present a deep reinforcement-learning-based
model that considers the effectiveness of leading autonomous vehicles in mixed-autonomy …

… information to vehicles through Vehicle-to-… Reinforcement Learning based approach that
integrates the data collected through sensing and connectivity capabilities from other vehicles …

… So we combine this framework with reinforcement learning in this paper to improve its
ability to deal with the highly dynamic environment. Considering the complexity of high-…

… 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-…

… 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 must achieve a left turn in an unsignalized intersection involving another vehicle …
We then show how to constrain a reinforcement learning agent to only choose among the …

… This might lead to serious safety issues because expert drivers generally do not provide
dangerous demonstrations so the autonomous vehicle cannot learn how to deal with those …