  Deep learning has provided new ways of manipulating, processing and analyzing
data. It sometimes may achieve results comparable to, or surpassing human
expert performance, and has become a source of inspiration in the era of
artificial intelligence. Another subfield of machine learning named
reinforcement learning, tries to find an optimal behavior strategy through
interactions with the environment. Combining deep learning and reinforcement
learning permits resolving critical issues relative to the dimensionality and
scalability of data in tasks with sparse reward signals, such as robotic
manipulation and control tasks, that neither method permits resolving when
applied on its own. In this paper, we present recent significant progress of
deep reinforcement learning algorithms, which try to tackle the problems for
the application in the domain of robotic manipulation control, such as sample
efficiency and generalization. Despite these continuous improvements,
currently, the challenges of learning robust and versatile manipulation skills
for robots with deep reinforcement learning are still far from being resolved
for real world applications.
