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.

中文翻译：

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增强 Q 模仿学习 (AQIL)

无监督学习的研究一般可以分为两类：模仿学习和强化学习。在模仿学习中，机器通过模仿专家系统的行为来学习，而在强化学习中，机器通过直接环境反馈进行学习。传统的深度强化学习在机器开始收敛到最佳策略之前需要很长时间。这篇论文提出了Augmented Q-Imitation-Learning，这是一种通过将Q-imitation-learning作为传统深度Q-learning的初始训练过程来加速深度强化学习收敛的方法。