论文标题
增强Q模仿学习（AQIL）
Augmented Q Imitation Learning (AQIL)
论文作者
论文摘要
对无监督学习的研究通常可以分为两类：模仿学习和强化学习。在模仿学习时，机器可以通过模仿专家系统的行为来学习，而在加强方面，学习机器通过直接的环境反馈学习。传统的深度强化学习需要大量时间才能开始融合最佳政策。本文提出了增强的Q iMation学习学习，这种方法可以通过将Q-imitation学习作为传统深度Q学习的初始训练过程来加速深度强化学习融合。
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.
