  Deep reinforcement learning (DRL) has made great achievements since proposed.
Generally, DRL agents receive high-dimensional inputs at each step, and make
actions according to deep-neural-network-based policies. This learning
mechanism updates the policy to maximize the return with an end-to-end method.
In this paper, we survey the progress of DRL methods, including value-based,
policy gradient, and model-based algorithms, and compare their main techniques
and properties. Besides, DRL plays an important role in game artificial
intelligence (AI). We also take a review of the achievements of DRL in various
video games, including classical Arcade games, first-person perspective games
and multi-agent real-time strategy games, from 2D to 3D, and from single-agent
to multi-agent. A large number of video game AIs with DRL have achieved
super-human performance, while there are still some challenges in this domain.
Therefore, we also discuss some key points when applying DRL methods to this
field, including exploration-exploitation, sample efficiency, generalization
and transfer, multi-agent learning, imperfect information, and delayed spare
rewards, as well as some research directions.
