  Combination of machine learning (for generating machine intelligence),
computer vision (for better environment perception), and robotic systems (for
controlled environment interaction) motivates this work toward proposing a
vision-based learning framework for intelligent robot control as the ultimate
goal (vision-based learning robot). This work specifically introduces deep
reinforcement learning as the the learning framework, a General-purpose
framework for AI (AGI) meaning application-independent and
platform-independent. In terms of robot control, this framework is proposing
specifically a high-level control architecture independent of the low-level
control, meaning these two required level of control can be developed
separately from each other. In this aspect, the high-level control creates the
required intelligence for the control of the platform using the recorded
low-level controlling data from that same platform generated by a trainer. The
recorded low-level controlling data is simply indicating the successful and
failed experiences or sequences of experiments conducted by a trainer using the
same robotic platform. The sequences of the recorded data are composed of
observation data (input sensor), generated reward (feedback value) and action
data (output controller). For experimental platform and experiments, vision
sensors are used for perception of the environment, different kinematic
controllers create the required motion commands based on the platform
application, deep learning approaches generate the required intelligence, and
finally reinforcement learning techniques incrementally improve the generated
intelligence until the mission is accomplished by the robot.
