Deep Reinforcement Learning (RL) is a rapidly evolving field in data science, leading to a growing demand for accessible and effective RL tools. A variety of Python libraries have been developed for implementing and testing RL models, each with unique features and limitations. Key libraries include KerasRL, Pyqlearning, Tensorforce, RL_Coach, TFAgents, Stable Baselines, and MushroomRL, among others. These libraries are evaluated based on criteria such as the number of state-of-the-art algorithms implemented, documentation quality, ease of code customization, environment support, logging tools, vectorized environment capabilities, and update frequency. While Tensorforce, Stable Baselines, and RL_Coach are highlighted as some of the best available options due to their comprehensive documentation and robust algorithm sets, others like Pyqlearning and KerasRL have significant drawbacks such as incomplete documentation and lack of updates. Ultimately, the choice of library depends on the specific requirements of the RL task, such as the need for experimentation with different algorithms or the integration of cutting-edge methods.