A Framework for Object Detection using Deep Reinforcement Machine Learning
Saurabh Tiwari1, S. Veena Dhari2

1Saurabh Tiwari*, Assistant Professor, Department of Computer Science and Engineering, Govt. Polytechnic College, Bhopal, (M.P), India.
2Dr. S. Veenadhari, Assistant Professor, Department of Computer Engineering, Rabindranath Tagore University, Bhopal, (M.P.), India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 31, 2020. | Manuscript published on April 10, 2020. | PP: 1508-1514 | Volume-9 Issue-6, April 2020. | Retrieval Number: B7480129219/2020©BEIESP | DOI: 10.35940/ijitee.B7480.049620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Machine learning area enable the utilization of Deep learning algorithm and neural networks (DNNs) with Reinforcement Learning. Reinforcement learning and DL both is region of AI, it’s an efficient tool towards structuring artificially intelligent systems and solving sequential deciding problems. Reinforcement learning (RL) deals with the history of moves; Reinforcement learning problems are often resolve by an agent often denoted as (A) it has privilege to make decisions during a situation to optimize a given problem by collective rewards. Ability to structure sizable amount of attributes make deep learning an efficient tool for unstructured data. Comparing multiple deep learning algorithms may be a major issue thanks to the character of the training process and therefore the narrow scope of datasets tested in algorithmic prisons. Our research proposed a framework which exposed that reinforcement learning techniques in combination with Deep learning techniques learn functional representations for sorting problems with high dimensional unprocessed data. The faster RCNN model typically founds objects in faster way saving resources like computation, processing, and storage. But still object detection technique typically require high computation power and large memory and processor building it hard to run on resource constrained devices (RCD) for detecting an object during real time without an efficient and high computing machine. 
Keywords: AI, Deep RL, Machine Learning, Reinforcement Learning.
Scope of the Article: Machine learning