Deep Reinforcement Learning (DRL) is data inefficient and may require large amounts of data through millions of iterations to give results for simple tasks. To circumvent this, simulated environments are often used to train DRL models. There are some major practical gaps between simulated and real environments and this makes the models difficult to operate feasibly in the real-world. This is why DRL is used widely in closed environments like video games, but is difficult to apply in real-world environments. Some organizations use this by opting for a deep learning platform (platforms with large data sets and documentation easily available) to help them implement DRL.