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 …

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years,
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …

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 …

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 …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

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 …

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

With the development of state-of-art deep reinforcement learning, we can efficiently tackle
continuous control problems. But the deep reinforcement learning method for continuous …

Most methods that attempt to tackle the problem of Autonomous Driving and overtaking
usually try to either directly minimize an objective function or iteratively in a Reinforcement …

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 …