## 1 Introduction  
Visual object tracking is a classic computer vision problem that entails detecting an object in a scene and distinguishing it from other objects in each frame. In sequential frames, it might be static or dynamic. To our knowledge, there are a slew of fundamental challenges to visual object tracking, including occlusion, motion blur, background clutter, ambient illumination fluctuations, etc. To address these issues, the most common tracking approaches [1, 2, 3, 4] monitor specific object classes utilizing a variety of feature learning algorithms. However, several other tracking techniques offer great efficiency and competitive outcomes when compared to the state-of-the-art object tracking models. Nevertheless, they still have constraints that need to be addressed in order to achieve high accuracy and quicker tracking performance under difficult environmental circumstances. There are certain tracking filters [5, 6] and object-detection-based models [7, 8] that may be competitive alternatives to classic methods, but they still have drawbacks when compared to deep network-based tracking strategies. Despite the relative success of conventional tracking approaches [9, 10, 11], deep convolutional neural network (Deep CNN)-based visual object tracking models [12, 13, 14] have gainedpopularity in recent years. The popularity and advantages of CNN-based trackers may be explained in two ways: firstly, by their tracking robustness, and secondly, by the highly efficient feature representations located inside their detection units. In the majority of target tracking situations, pretrained CNN classifiers were employed [11; 15; 16] for finding objects and classifying them, or cropping and regressing methods [9; 10; 17]. However, the disparity between the CNN-based feature representation for classification and the tracking algorithm has an impact on the output of the final tracking results. Furthermore, the pretrained CNN classifier fails to function well in the challenging environment of the tracking process, where the captured spatial features are not explored thoroughly during training. Nonetheless, tracking by detection method has been proven to be effective in numerous difficult tracking circumstances [11; 18; 19] where CNN was used in training and superior tracking results were finally obtained compared to standard target trackers. Unfortunately, in the event of a visually crowded scene with numerous occluded frames and a small distance of correlation, targeted items may be missed. The basic strategy and goal of CNN-based models relies on a target classification approach. In addition, an object classification model will be used to address a backdrop cluttering problem. Consequently, there is a necessity to investigate the ability of deep learning models to automatically learn effective features in a virtual environment that uses drone agents with spatial and temporal constraints. In particular, it should be considered to make a long-short term feature learning strategy and classify objects into identical target classes with the proposed end-to-end model. The motivation for this study was to develop an object tracking algorithm capable of learning and tracking the target using deep reinforcement learning and an artificial intelligence network model in a wide variety of skills and abilities that can compete with other models in complicated tasks. In this study, we created a novel tracker that was based on a sequential recurrent neural network [20; 21] prediction and tracking architecture. The deep reinforcement learning-based Q-network agent tracker was integrated with the high-dimensional cross-platform virtual simulator for drones called AirSim. For testing reasons, we built our unique tracking model using the AirSim [22] (Aerial Informatics and Robotics Simulation) simulator platform. The platform enables the algorithm to be evaluated in a realistic virtual environment that includes elements such as pedestrians, automobiles, trees, street signs, buildings, and weather conditions.