<![CDATA[Accurate control robotic arms in two-dimensional environments present significant challenges, particularly in dynamic, real-time applications. Traditional model-based approaches require substantial system modeling, rendering them computationally extensive. This paper presents an adaptive Artificial Intelligence (AI)-driven approach through the use of Deep Q-Networks (DQN) control for a two–link robotic arm thus supporting better scalability. The DQN algorithm, a model-free Reinforcement Learning (RL) technique, allows the robotic arm to independently learn optimal control strategies by interaction with the environment and adapting to dynamic conditions. The task of the robot established reaches a specific target (red point) within a limited number of episodes. Key components of the methodology contain problem statement, DQN architecture, representation of the state and action spaces, a reward function, and the training process. Experimental results indicate that the DQN agent effectively learns to find optimal actions with high accuracy and robustness in guiding the arm to the target. The performance steadily improves during initial training, followed by stabilization, indicating an effective control policy. This study contributes to the knowledge of reinforcement learning in robotic control tasks and demonstrates, in particular, the potential of DQN for solving complex, goal-oriented tasks with minimal prior modeling. Compared to conventional control approaches, the DQN-driven one reveals higher flexibility, scalability, and efficiency. Although carried out in a simplified 2D environment, the novelty of this research lies in its emphasis on enabling the robotic arm to accomplish goal-oriented reaching tasks, lays a strong foundation for future applications in industrial automation and service robotics.]]>
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