Reinforcement learning for optimization

Reinforcement learning has been successful and applied in many areas of AI and beyond. This success can be attributed to the philosophy of the underlying data behind machine learning, which supports the automatic discovery of patterns from the data instead of manual methods using expert knowledge.

Here are some points that will help you guys to understand reinforcement learning optimization more clearly

Learn to lift

Review the general performance of continuous optimization algorithms. They work repeatedly and maintain some iteration, which is the central part of the objective function. At first, the iteration is random in the domain. At each time, the step vector is calculated using a fixed update method, which is used to update the iteration. This update process is often a function of the history of functional objective gradients evaluated in the present and past periods.

Learning to learn

Consider the case where the objective functions are loss functions to train other models. With this setting, we can use an optimizer for “learning to learn.” For clarity, we will refer to the model prepared using the optimizer as the “base model” and prefix the familiar words “base-” and “meta-” to break down the related concepts.

What exactly does ‘learning to learn’ mean?

Although this word appears from time to time in newspapers, different authors have used it to refer to other things, and there has yet to be a consensus on its exact meaning.

Often, it is also used with the term “meta-learning.” Learn what you have to learn

These methods aim to learn basic modeling principles sound in a family of related tasks. Meta-knowledge captures standard features within families, so essential learning about new family roles can be done quickly. Examples include transfer learning, multi-task learning, and crash learning.

Learning how to learn

While the methods in the previous sections seek to know more about the learning outcomes, the ways in this section seek to understand more about the learning process. Meta-knowledge captures the standard features and behaviors of learning algorithms. There are three things under this scope:

  • the base model
  • the base algorithm for training the base model
  • the meta-algorithm that learns the base algorithm

What is learned is not the core model but the core algorithm, which trains the model and the function.


Learning each model requires training on a small number of examples and clustering with a large class from which examples are drawn. Therefore, it is instructive to consider measures in the class corresponding to our situation of learning optimizers for basic model training. Each sample is an objective task, which corresponds to the task of death to train the leading model in the study. The job is characterized by a set of models and accurate predictions, or in other words, data input, which is used to train the base model. Meta classifiers have multiple objective functions, and meta-analysis methods have different objective functions assigned to the same class. Objective functions can be different in two ways: they can correspond to different main types or parts. Therefore, clustering means the learner works in other settings or jobs.

Why is it important?

Let’s assume that we don’t care about collections. In this case, we will analyze the optimization on the same objective function used to train the optimization. If we use only one objective function, the best optimizer will be the one at the top of the best: an optimizer always converges to the maximum in one step, regardless of the start. In our case, the objective function corresponds to the loss of training a single base model in a single operation; thus, the optimization takes the weight of the base model into account. Even if we work with multiple targets, the learner can try to identify the target of the task and jump to the saved location as soon as it happens.

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