Towards enabling deep learning techniques for adaptive dynamic programming
Zhen Ni, Naresh Malla, Xiangnan Zhong · 2017
Human-level control through deep learning and deep reinforcement learning have revealed the unique and powerful potentials through a very complex Go game. The AlphaGo, developed by Google DeepMind, has beat the top Go game player early this year. The scientific and technological advancement behind the success of AlphaGo attracted researchers from multiple areas, including machine learning, artificial intelligence, computational intelligence and so on. Adaptive dynamic programming (ADP) methods have the similar fundamental principle with reinforcement learning, and show strong performance for continuous time and continuous state systems. Deep learning techniques are also possible to be integrated for ADP designs. In this paper, we discuss the key techniques and components in deep reinforcement learning and then present the successful applications for computer games and maze navigation. Future opportunities for deep learning enabled ADP will be discussed at the end.