… hybrid Deep Reinforcement learning and Genetic algorithm for Smart-Platooning (DRG-SP)
the AVs. The leverage of the deep reinforcement … Algorithm in Deep Reinforcement learning …

… the gap between the requirements of autonomous driving and a limited amount of knowledge,
we propose to extract rules from the reinforcement learning process to expand the ES’s …

… A wide range of techniques in Machine Learning itself have been developed, and this
article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides …

… complex driving scenarios. This paper proposes a hybrid reinforcement learning (RL) based
Eco-Driving … A deep deterministic policy gradient (DDPG) algorithm is designed to learn the …

… In this context, the aim of this work is to design an effective hybrid two-… Deep Reinforcement
Learning (DRL) in combination with model-based approaches, lets the autonomous vehicles …

… a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for
autonomous vehicles to … learning (RL)-based energy management in hybrid electric vehicles, RL-…

… eco-driving problem for HEVs with the capability of autonomously … eco-driving control of
connected and autonomous vehicles … Bideaux, “Vehicle trajectory optimization for hybrid vehicles …

… To this end, we developed the first hybrid solution, named … -car accident scenarios based
on the German in-depth road … by autonomous cars is defined in section 2, and our hybrid solu…

… autonomous driving of intelligent hybrid electric vehicles. In this … by Deep Reinforcement
Learning. After modeling a typical car-following scene, the leading car is detected in the driving …

… The inverse reinforcement study is a field of study of the agent’s intentions, values, or … a
Hybrid Deep Anomaly Detection (HDAD) framework that combines multi-agent Reinforcement …