关键词: 深度学习, 强化学习, 深度逆向强化学习

Abstract: Deep inverse reinforcement learning is a new research hotspot in the field of machine learning. It aims at recovering the reward function of deep reinforcement learning by the experts’ example trajectories. This paper systematically introduces three kinds of classic deep reinforcement learning methods. Then inverse reinforcement learning algorithms including apprenticeship learning, max margin plan, structured classification and probability models are described; then, some frontier researches of deep inverse reinforcement learning are reviewed, including the deep max margin plan inverse reinforcement learning, deep inverse reinforcement learning based on DQN and deep maximum entropy inverse reinforcement learning and recovering reward functions from non-expert trajectories etc. Finally, the existing issues and development direction are summarized.

Key words: deep learning, reinforcement learning, deep inverse reinforcement learning