Augmented Q Imitation Learning (AQIL) [article]

Xiao Lei Zhang, Anish Agarwal
2020 arXiv   pre-print
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
more » ... reinforcement learning convergence can be accelerated by applying Q-imitation-learning as the initial training process in traditional Deep Q-learning.
arXiv:2004.00993v2 fatcat:nwtzjuijwfgqfd3paiu34iy7my