The recent development of object-tracking framework inventions has affected the performance of many manufacturing and service industries, such as product delivery, autonomous driving systems, security systems, military and transportation, retailing industries, smart cities, healthcare systems, agriculture, etc. Object tracking in physical environments and conditions is much more challenging to achieve accurate results. However, the process can be experimented using simulation techniques or platforms to evaluate and check the model's performance under different simulation conditions and weather changes. This paper represents one of the target tracking approaches based on the reinforcement learning technique integrated with tf-agent (TensorFlow-Agent) to accomplish the tracking process in the Unreal Game Engine simulation platform, Blocks. The productivity of these platforms can be seen while experimenting in virtual-reality conditions with virtual drone agents and performing fine-tuning to achieve the best or desired performance. In this proposal, the tf-agent drone learns how to track an object integration with a deep reinforcement learning process to control the actions, states, and tracking by receiving sequential frames from a simple Blocks environment. The TF-agent is trained in a Blocks environment for adaptation to the environment and existing objects in a simulation environment for further testing and evaluation regarding the accuracy of tracking and speed. We have tested and compared two approaches to the algorithm methods based on the DQN and PPO trackers integrated with the simulation process regarding stability, rewards, and numerical performance.