Despite promising progress in reinforcement learning (RL), developing algorithms for
autonomous driving (AD) remains challenging: one of the critical issues being the absence …

Reinforcement learning (RL) has shown to reach super human-level performance across a
wide range of tasks. However, unlike supervised machine learning, learning strategies that …

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

Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks
and shown promise in autonomous driving (AD) as well. Considering the distinctiveness of …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

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

We introduce ApolloRL, an open platform for research in reinforcement learning for
autonomous driving. The platform provides a complete closed-loop pipeline with training …

The unprotected left turn is one of the most difficult problems in real-world autonomous
driving. Its difficulty is due to the diverse and hard-to-predict interactions among possibly …

Reinforcement learning (RL) is a powerful data-driven control method that has been largely
explored in autonomous driving tasks. However, conventional RL approaches learn control …

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 …