  Deep artificial neural networks, trained with labeled data sets are widely
used in numerous vision and robotics applications today. In terms of AI, these
are called reflex models, referring to the fact that they do not self-evolve or
actively adapt to environmental changes. As demand for intelligent robot
control expands to many high level tasks, reinforcement learning and state
based models play an increasingly important role. Herein, in computer vision
and robotics domain, we study a novel approach to add reinforcement controls
onto the image recognition reflex models to attain better overall performance,
specifically to a wider environment range beyond what is expected of the task
reflex models. Follow a common infrastructure with environment sensing and AI
based modeling of self-adaptive agents, we implement multiple types of AI
control agents. To the end, we provide comparative results of these agents with
baseline, and an insightful analysis of their benefit to improve overall image
recognition performance in real world.
