Papers › Augmented Q Imitation Learning (AQIL)

Augmented Q Imitation Learning (AQIL)

31 Mar 2020arXiv:2004.00993archive 2025-07-28

Xiao Lei Zhang, Anish Agarwal

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.

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Deep Reinforcement Learning Imitation Learning Q-Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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