… the Reinforcement Learning approach for learning the automated lane change behavior …
The state space and action space are both treated as continuous to learn a more practical …

… Abstract—This paper introduces a method, based on deep reinforcement learning, for …
trained in a simulated environment to handle speed and lane change decisions for a truck-trailer …

… The third part is the DQN algorithm, which is in charge of high-level lane change decision-making.
In this study, we aim at deep reinforcement learning based lateral lane change …

… a lane change decision-making framework based on deep reinforcement learning to find a …
the minimum expected risk using deep reinforcement learning. Finally, our proposed methods …

… issues by presenting a reinforcement learning-based … reinforcement learning (RL) agent
learn to drive as close as possible to a desired velocity by executing reasonable lane changes …

… However, a direct application of reinforcement learning algorithm for automated driving still
… reinforcement learning based architecture for decision making and control of lane changing …

… propose to use a deep reinforcement learning-based method that can learn sub-policies for
… hierarchical deep reinforcement learning algorithm that can deal with lane change behaviors …

… lane change actions [10]. In this paper, we study the emerging reinforcement learning (RL)
[11], [12] based lane change. … an automated vehicle to gradually learn how to drive through …

… reinforcement learning approach for robust lane change … is presented to model lane change
decision making behaviors … to optimize autonomous driving lane change policies while …

… This model was integrated into deep reinforcement learning (DRL) to find strategies with
minimum expected risk. Finally, the proposed method was evaluated in three lane change …