Exploring and Analysing Deep Reinforcement Learning Based Algorithms for Object Detection and Autonomous Navigation

Object recognition algorithms need to be very accurate and efficient in order for autonomous navigation systems to see and securely interact with their surroundings. Although conventional computer vision techniques have made significant advancements in this area, they sometimes have trouble with complicated situations, occlusions, and dynamic settings. This research study introduces and investigates deep reinforcement learning (DRL) methodologies to enhance object recognition in the context of autonomous navigation. A potent paradigm for addressing challenging decision-making issues is deep reinforcement learning. The purpose of this study is to show how agents may learn to identify and categorize objects in a variety of demanding situations by defining object detection as a reinforcement learning problem.