This project investigates the challenges and opportunities of reinforcement learning (RL) in robotics by analyzing its current applications and trends. It explores how RL enables robots to learn through interaction with their environments, identifying key fields such as navigation, motion planning, and object recognition. The research highlights technical obstacles, such as biases in AI models, scalability issues, and adapting systems to real-world conditions. Through an analysis of 36 high-quality studies, the project proposes future directions for advancing RL in robotics, including improved simulations, centralized robotic databases, and the use of pre-trained models to foster ethical and effective technological innovation.