Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are
still inefficient and limited in terms of cooperating with each other or coordinating with …

Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to
its potential to solve complex classification and control problems. However, existing RL …

Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …

The capability to learn and adapt to changes in the driving environment is crucial for
developing autonomous driving systems that are scalable beyond geo-fenced operational …

In autonomous driving, the ego vehicle and its surrounding traffic environments always have
uncertainties like parameter and structural errors, behavior randomness of road users, etc …

Advanced deep reinforcement learning shows promise as an approach to addressing
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …

Deep reinforcement learning (DRL) has a great potential for solving complex decision-
making problems in autonomous driving, especially in mixed-traffic scenarios where …

Reinforcement Learning (RL) methods are regarded as effective for designing autonomous
driving policies. However, even when RL policies are trained to convergence, ensuring their …

With the advent of universal function approximators in the domain of reinforcement learning,
the number of practical applications leveraging deep reinforcement learning (DRL) has …