The cosmic microwave background holds multiple clues to the development of the universe. Because of its cosmic nature, it is almost impossible for researchers to access this information by conventional means. However, with the help of a generative adversarial network (GAN) augmented with a deep learning approach, they could finally change this. A generative adversarial network is a rule-based, machine-learning technique that uses opportunities in data and fantasy worlds. The author of this research implements GAN for the cosmic microwave background (CMB) separation problem. They report the results by leveraging machine learning premised on GAN. To validate the robustness of our network, they perform several tests against different foreground models by increasing the amplitude of each component.

Reinforcement learning is when the system is allowed to make its own decisions based on what it learns. There are 2 types of observations, formative and summative. These observations have been identified as crucially important for neural network training of complicated tasks with conditional control flow. The central theme of this paper is applying reinforcement learning to follow instructions with complex control-flow. The authors study a special but important subset of multi- task reinforcement learning problems, namely instructions with complex control-flow in this work. They develop an encoding and attention architecture to achieve the research objective.