Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …

This paper investigates how a Bayesian reinforcement learning method can be used to
create a tactical decision-making agent for autonomous driving in an intersection scenario …

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 …

Reinforcement learning (RL) can be used to create a decision-making agent for autonomous
driving. However, previous approaches provide black-box solutions, which do not offer …

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

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Designing reliable decision strategies for autonomous urban driving is challenging.
Reinforcement learning (RL) has been used to automatically derive suitable behavior in …

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …

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

Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …