Reinforcement learning (RL) has emerged as a powerful approach to enable autonomous robots to learn optimal behaviors through interactions with their environment. As robots increasingly become integral in diverse fields such as manufacturing, healthcare, and autonomous vehicles, RL offers a promising framework for addressing the complexity of real-world decision-making. However, deploying RL for autonomous robotics presents several challenges that must be overcome to ensure efficiency, safety, and adaptability. These challenges include sample inefficiency, the need for robust reward engineering, dealing with the high dimensionality of real-world environments, and ensuring safe human-robot interaction in shared spaces. One of the main challenges in applying RL to autonomous robotics is the high computational cost and time required for training robots through trial and error. The exploration of vast environments can lead to costly failures and slow learning. To mitigate this, innovations such as hierarchical reinforcement learning, transfer learning, and simulation-based training have been developed, allowing robots to learn faster and more efficiently. Moreover, reward shaping and inverse reinforcement learning (IRL) have advanced the design of reward functions, enabling robots to learn more complex tasks by mimicking human-like behavior and preferences.