  In the last decade, deep learning has achieved great success in machine
learning tasks where the input data is represented with different levels of
abstractions. Driven by the recent research in reinforcement learning using
deep neural networks, we explore the feasibility of designing a learning model
based on expert behaviour for complex, multidimensional tasks where reward
function is not available. We propose a novel method for apprenticeship
learning based on the previous research on supervised learning techniques in
reinforcement learning. Our method is applied to video frames from Atari games
in order to teach an artificial agent to play those games. Even though the
reported results are not comparable with the state-of-the-art results in
reinforcement learning, we demonstrate that such an approach has the potential
to achieve strong performance in the future and is worthwhile for further
research.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition