Making decisions in complex driving environments is a challenging task for autonomous
agents. Imitation learning methods have great potentials for achieving such a goal …

Designing a safe and human-like decision-making system for an autonomous vehicle is a
challenging task. Generative imitation learning is one possible approach for automating …

In this paper, we consider autonomous driving of a vehicle using imitation learning.
Generative adversarial imitation learning (GAIL) is a widely used algorithm for imitation …

Deep reinforcement learning (DRL) has been demonstrated to be effective for several
complex decision-making applications, such as autonomous driving and robotics. However …

In many real-world applications where specifying a proper reward function is difficult, it is
desirable to learn policies from expert demonstrations. Adversarial Inverse Reinforcement …

Driver behavior modeling is an important task for predicting or simulating the evolution of
traffic situations. We investigate the use of Adversarial Inverse Reinforcement Learning …

The generative adversarial imitation learning (GAIL) has provided an adversarial learning
framework for imitating expert policy from demonstrations in high-dimensional continuous …

Generative adversarial imitation learning (GAIL) has shown promising results by taking
advantage of generative adversarial nets, especially in the field of robot learning. However …

Deep Imitation Learning requires a large number of expert demonstrations, which are not
always easy to obtain, especially for complex tasks. A way to overcome this shortage of …

Imitation learning is well-suited for robotic tasks where it is difficult to directly program the
behavior or specify a cost for optimal control. In this work, we propose a method for learning …