This article discusses the concept of world models - generative neural network models that allow agents to simulate and learn within their own dream environments. Agents can be trained to perform tasks within these simulations and then apply the learned policies in real-world scenarios. The study explores this approach within the context of reinforcement learning environments, highlighting its potential for efficient learning and policy transfer. The integration of iterative training procedures and evolution strategies further supports the scalability and applicability of this method to complex tasks.