… the application of the Policy Gradient reinforcement learning method in the area of vehicle
… placed in a simulated highway environment, by using reinforcement learning approach. The …

… ficult task for intelligent vehicles in dynamic transportation environments. In this paper, a
reinforcement learning approach with value function approximation and feature learning is …

… The environment presented in this paper provides a highway traffic scenario for the learning
… learn the successful and effective behavior in our Pythonbased highway environment. The …

… In this paper, we aim to find the optimal action during highway driving, a simulating
environment including three lanes and 20 randomly distributed surrounding vehicles is thus …

… Learning approach based on CNNs to extract deep representations of urban environment …
Other approaches address the problem of road user detection in corners and intersections in …

… deep reinforcement learning approach is proposed to handle tactical driving in complex
highway traffic environments … The core of our deep reinforcement learning approach is a deep Q-…

… of (deep) reinforcement learning models in vehicle longitudinal control … road environments.
The reward function of a (D)RL framework is a critical component in the DRL agent’s learning …

… This following organization of this article is given as follows: the highway driving environment
and the control modules of the ego and surrounding vehicles are described in Section II. …

… Tactical driving decisions of unmanned ground vehicles in complex highway environments:
a deep reinforcement learning approach. Proc IMechE, Part D: J Automobile Engineering …

… -making on highway environments. More specifically, we present a novel approach that is
able … , we implement all methods that we introduced in Section IV on the environment in [23]. In …