Adaptive decision-making in dynamic contexts is often necessary for difficult activities like robotics, where reinforcement learning (RL) has become a potent model for training autonomous agents. In this work, current developments in reinforcement learning approaches for robotics are reviewed in detail. Deep reinforcement learning (DRL) and other more modern techniques, such as policy gradient methods and Q-learning, are among the many RL algorithms covered in the first section. By considering elements like sample efficiency, scalability, and resistance to noise and uncertainty, these algorithms are assessed for their applicability for various robotic applications. To improve learning efficiency and transferability in robotic tasks, the chapter also addresses the integration of reinforcement learning (RL) with other machine learning approaches, including inverse reinforcement learning and imitation learning. The second part of this chapter looks at the wide spectrum of robotics applications of reinforcement learning, including as task planning, navigation, control, and manipulation. Key issues and current developments are covered for each application domain, together with information on the deployment and practical application of RL-based robotic systems. The study also showcases prominent real-world implementations and success stories of reinforcement learning in robotic applications, demonstrating the technology’s potential to transform a range of industries, including manufacturing, logistics, healthcare, and service robots. The final half of the study discusses the difficulties and restrictions that reinforcement learning in robotics faces, despite its exciting potential. These include problems with sample inefficiency, safety issues, and applying newly learned policies to a variety of settings and jobs. The article also addresses current initiatives to address these issues by using strategies including curriculum learning, reward shaping, and safety-aware reinforcement learning.