Deep Reinforcement Learning: Methods and Applications

Dear Colleagues,

Real-world problems are increasingly complex, and applications of traditional reinforcement learning (RL) methods to solve these problems are becoming more and more challenging. Fortunately, deep learning has emerged as a powerful tool, and with the great capability of function approximation and representation learning, it is an excellent complement to traditional RL methods. The combination of deep learning and RL, namely deep RL, has made breakthroughs in developing artificial agents that can perform at human-level. Deep RL methods have been able to solve many complex problems in different domains from video games (e.g., Atari games, the game of Go, the real-time strategy game StarCraft II, the 3D multiplayer game Capture the Flag in Quake III Arena, and the teamwork game Dota 2) to real-world applications such as robotics, autonomous vehicles, autonomous surgery, biological data mining, drug design, cybersecurity, and the internet of things.

This Special Issue focuses on methods and applications of deep RL. We would like to invite papers proposing advanced deep RL methods and/or their novel applications to solve complex problems in various domains.

Dr. Thanh Thi Nguyen
Assoc. Prof. Peter Vamplew
Guest Editors

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