What is the limit creating simulation environments ?

Creating simulation environments is an essential component of Deep Reinforcement Learning (DRL), as it allows agents to be trained on a vast amount of data generated on-the-fly. However, there are limits to the complexity and fidelity of these environments. One major limit is the accuracy of the model used to simulate the environment. While simple environments can be modeled accurately using relatively straightforward techniques, more complex environments may require more sophisticated models that take into account a wide range of variables and interactions. Creating an accurate model of such complex environments can be a significant challenge. Another limit is the computational resources required to generate and simulate the environment. Creating a high-fidelity simulation environment can require a significant amount of computing power, which may be prohibitively expensive or time-consuming to obtain.