… of the autonomous vehicle is obtained as … autonomous vehicle using reinforcement learning
techniques. Second, we collect a number of demonstrations from an expert driver and learn …

… Abstract— We present a control approach for autonomous vehicles based on deep
reinforcement learning. A neural network agent is trained to map its estimated state to acceleration …

… This algorithm extends the general Q-Learning reinforcement learning algorithm to adapt
to an infinitely larger state space. In vanilla Q-Learning, the algorithm learns an action-value …

… 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 …

… , thus, to propose a novel adversarial deep reinforcement learning (RL) framework that aims
… from intravehicle sensors such as camera, radar, RSSs, and intervehicle beaconing. We …

… autonomous vehicle must ensure functional safety and be able to deal with urgent events.
For example, vehicles … the gap between autonomous driving and reinforcement learning, we …

… Q-learning algorithm is unstable in some games in the Atari 2600 domain. In this paper, a
reinforcement learning approach called Double Q-learning is used to control a vehicle’s speed …

… knowledge because only an evaluative signal is required for reinforcement learning, and
is more applicable to real systems because we combine reinforcement learning with an FIS. …

… In this review we cover the notions of reinforcement learning, … Reinforcement learning
methods were developed to handle … Autonomous vehicle stochastic control is large domain, and …

… on reinforcement learning for autonomous vehicles decision… proves that the reinforcement
learning system is more … based on reinforcement learning for autonomous vehicle decision…