Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

In recent years, significant progress has been made in solving challenging problems across
various domains using deep reinforcement learning (RL). Reproducing existing work and …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the
compositional, relational, and hierarchical structure of the world, and learn to transfer it to the …

It is expected that autonomous vehicles capable of driving without human supervision will be
released to market within the next decade. For user acceptance, such vehicles should not …

Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

This paper presents a general framework for exploiting the representational capacity of
neural networks to approximate complex, nonlinear reward functions in the context of …

Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …