Since traditional reinforcement learning (RL) approaches need active online interaction with
the environment, previous works are mainly investigated in the simulation environment …

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 …

In many real-world decision making problems, reaching an optimal decision requires taking
into account a variable number of objects around the agent. Autonomous driving is a domain …

Accurate behavior anticipation is essential for autonomous vehicles when navigating in
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …

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 …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …

Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated
negotiation skills with other road users when overtaking, giving way, merging, taking left and …

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 …

Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …

Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …