Optimization involces the process of choosing the best solution from a set of available alternatives. In optimization problems, the objective is to either maximize or minimize a real function by methodically selecting input values from a predefined set and caculating the function’s corresponding value.

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.      RL is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Deep learning, a subset of machine learning, relies on artificial neural networks with representation learning. Its algorithms aim to extract essential information or functions from extensive and complex data through a combination of nonlinear transformation techniques. This approach enables the creation of high-level abstractions, making it suitable for handling large datasets and intricate materials across various domains.