Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …

Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have
demonstrated impressive performance in complex tasks, including autonomous driving. The …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but
agents often fail to generalize beyond the environment they were trained in. As a result …

Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …

Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL
models typically involves in a multi-step process: pre-training RL models on simulators …

Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own
experiments and not rule-based control methods. However, there is no RL algorithm yet …

Existing approaches for improving generalization in deep reinforcement learning (RL) have
mostly focused on representation learning, neglecting RL-specific aspects such as …

In recent years, the subject of deep reinforcement learning (DRL) has developed very
rapidly, and is now applied in various fields, such as decision making and control tasks …

Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …