Discussing the article: "Neural networks made easy (Part 42): Model procrastination, reasons and solutions"
Dmitriy,
I am following your articles to learn as much as possible as your knowledge and expertise is way beyond me. After reading the article, it occurred to me that while the final model presented is excellent at identifying short trades and totally unsuccessful at identifying long trades, it could be part of a two tier trading solution. A long trade model is needed to complement the short trades. Do you think the long model could be developed by reversing some of the assumptions or is a wholly new model required, such as toe Go Explore in article #39?
Cheers on your current efforts and support for your future endeavors
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Check out the new article: Neural networks made easy (Part 42): Model procrastination, reasons and solutions.
In the context of reinforcement learning, model procrastination can be caused by several reasons. The article considers some of the possible causes of model procrastination and methods for overcoming them.
One of the main reasons for model procrastination is an insufficient training environment. The model may encounter limited access to training data or insufficient resources. Solving this problem involves creating or updating the dataset, increasing the diversity of training examples and applying additional training resources, such as computing power or pre-trained models for transfer training.
Another reason for model procrastination may be the complexity of the task it should solve or using a training algorithm that requires a lot of computing resources. In this case, the solution may be to simplify the problem or algorithm, optimize computational processes and use more efficient algorithms or distributed learning.
A model may procrastinate if it lacks motivation to achieve its goals. Setting clear and relevant goals for the model, designing a reward function that incentivizes the achievement of these goals and using reinforcement techniques, such as rewards and penalties, can help solve this problem.
If the model does not receive feedback or is not updated based on new data, it may procrastinate in its development. The solution is to establish regular model update cycles based on new data and feedback, and to develop mechanisms to control and monitor learning progress.
It is important to regularly evaluate the model's progress and learning outcomes. This will help you see progress made and identify possible problems or bottlenecks. Regular assessments will allow timely adjustments to be made to the training process to avoid delays.
Author: Dmitriy Gizlyk