The application of deep reinforcement learning in driving policy learning for automated
vehicles is limited by the difficulty of designing reward functions. Most existing inverse …

To learn a reward function that a driver adheres to is of importance to the human-like design
of autonomous driving systems. Inverse reinforcement learning (IRL) is one of the recent …

Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …

The present study proposes a framework for learning the car‐following behavior of drivers
based on maximum entropy deep inverse reinforcement learning. The proposed framework …

Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human
drivers with advanced computer-aided decision-making systems. However, for AVs to …

Inverse reinforcement learning (IRL) has been successfully applied in many robotics and
autonomous driving studies without the need for hand-tuning a reward function. However, it …

Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …

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 …

Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …

Inverse Reinforcement Learning (IRL) is formulated within the framework of Markov decision
process (MDP) where we are not explicitly given a reward function, but where instead we …