Applying reinforcement learning (RL) to real-world problems presents challenges such as sample efficiency, exploration vs. exploitation, non-stationarity, and safety concerns. RL algorithms often need vast data, which is impractical in many scenarios; this can be mitigated with model-based RL, transfer learning, and leveraging prior knowledge. Balancing exploration and exploitation is tough, especially in risky environments; safe exploration techniques and curiosity-driven approaches can help. Non-stationarity, where environment dynamics change, can be addressed with adaptive algorithms. Ensuring safety and robustness in RL applications requires rigorous testing and incorporating safety constraints during learning.