Utilizing simulation environments for generating training data in reinforcement learning (RL) offers numerous advantages, especially in domains like mathematics and physics. These advantages stem from the ability of simulations to provide a controlled, scalable, and flexible environment for training agents, which is important for developing effective RL algorithms. This approach is particularly beneficial due to