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Reinforcement Learning (RL) is a machine learning technique where an agent interacts with an environment, learning through trial and error to maximize cumulative rewards. While RL has achieved significant success in simulations and controlled settings, real-world applications remain challenging due to several limitations. One key issue is data inefficiency, as RL algorithms require vast amounts of data to learn effectively. This dependency often makes real-world data collection expensive and impractical, forcing reliance on simulated environments that may not accurately reflect real-world conditions. Additionally, high computational costs and resource intensiveness make RL difficult to implement at scale. Training sophisticated models like AlphaGo demands thousands of GPUs running for extended periods, making RL solutions impractical for many organizations. Another significant limitation is the lack of robustness and reliability in RL models, which struggle to generalize beyond their training scenarios. Small environmental changes, adversarial conditions, or unexpected shifts in input data can degrade performance, making RL less viable for safety-critical applications such as autonomous vehicles and healthcare. Practical barriers like complex model design, expensive real-world testing, and challenges in defining appropriate reward functions further hinder RL adoption. Ethical concerns also arise, as poorly tuned RL systems may behave unpredictably. In industries like finance, robotics, and healthcare, RL models often face reliability issues due to data scarcity, unpredictability, and high implementation costs. To make RL more practical, future research should focus on improving sample efficiency, reducing computational demands, and enhancing model robustness, ultimately bridging the gap between theoretical success and real-world applicability.Read less