Autonomous car racing is a challenging task in the robotic control area. Traditional modular
methods require accurate mapping, localization and planning, which makes them …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

We address the problem of learning autonomous driving behaviors in urban intersections
using deep reinforcement learning (DRL). DRL has become a popular choice for creating …

Reinforcement learning is considered as a promising direction for driving policy learning.
However, training autonomous driving vehicle with reinforcement learning in real …

Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …

Reinforcement learning (RL) has emerged as a powerful paradigm for training autonomous
software agents to make decisions in complex and dynamic environments. This abstract …

Reinforcement learning (RL) requires skillful definition and remarkable computational efforts
to solve optimization and control problems, which could impair its prospect. Introducing …

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 …

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which
can be extended to solve some complex and realistic decision-making problems …

The state-of-the-art decision and planning approaches for autonomous vehicles have
moved away from manually designed systems, instead focusing on the utilisation of large …