Triple

Target class: gradient-based optimization algorithm
Context triple: [Adam, instanceOf, gradient-based optimization algorithm]

• A. adaptive learning rate method chosen
  An adaptive learning rate method is an optimization technique that automatically adjusts the step size for each parameter during training based on past gradient information to improve convergence speed and stability.
• B. optimization paradigm
  An optimization paradigm is a conceptual framework that defines how to formulate, search for, and evaluate solutions to a problem in order to find the best (or sufficiently good) outcome under given constraints and objectives.
• C. policy gradient algorithm
  A policy gradient algorithm is a reinforcement learning method that directly optimizes a parameterized policy by estimating and following the gradient of expected cumulative reward with respect to the policy parameters.
• D. model-based reinforcement learning algorithm
  A model-based reinforcement learning algorithm is a decision-making method that learns or uses an explicit model of the environment’s dynamics to plan and select actions that maximize long-term rewards.
• E. algorithm
  An algorithm is a finite, well-defined sequence of computational steps or rules designed to solve a specific problem or perform a particular task.
• F. None of above.