  The study of unsupervised learning can be generally divided into two
categories: imitation learning and reinforcement learning. In imitation
learning the machine learns by mimicking the behavior of an expert system
whereas in reinforcement learning the machine learns via direct environment
feedback. Traditional deep reinforcement learning takes a significant time
before the machine starts to converge to an optimal policy. This paper proposes
Augmented Q-Imitation-Learning, a method by which deep reinforcement learning
convergence can be accelerated by applying Q-imitation-learning as the initial
training process in traditional Deep Q-learning.
