The field of robotics and autonomous systems has witnessed significant advancements in recent years, with an increasing focus on enhancing the capabilities of robotic agents through RL. This project centres around applying Reinforcement Learning (RL) techniques to do object manipulation tasks with a specific focus on making a robot arm reach a target object. Using a combination of Gazebo and Robot Operating System (ROS) environments, the robot arm is trained using custom OpenAI Gym environments to simulate the task. The primary objective involves positioning the end-effector of the robot arm close to a designated object and overcoming challenges such as self-collisions during movements. Various iterations of RL training, including different reward logics, curriculum learning approaches, and fine-tuning parameters, are explored to refine the decision-making capabilities of the agent. The training process of Curriculum Learning involves a phased approach, starting with basic movements and progressing to more complex tasks, demonstrating improved performance. However, challenges such as prolonged training times and uncertainties in arm behaviour persist. The project highlights the complexities inherent in designing effective RL strategies for robotic systems and stresses the need for further research to enhance computational efficiency and reliability in real-world applications.