Artificial Intelligence

    

Complexification Through Gradual Involvement And Reward Providing in Deep Reinforcement Learning

Authors: Eugene Rulko

Training a relatively big neural network that has enough capacity for complex tasks is challenging. In real life the process of task solving requires system of knowledge, where more complex skills are built upon previously learned ones. The same way biological evolution builds new forms of life based on a previously achieved level of complexity. Inspired by that, this work proposes ways of increasing complexity, especially a way of training neural networks with smaller receptive fields and using their weights as prior knowledge for more complex successors through gradual involvement of some parts, and a way where a smaller network works as a source of reward for a more complicated one. That allows better performance in a particular case of deep Q-learning in comparison with a situation when the model tries to use a complex receptive field from scratch.

Comments: 8 Pages.

Download: PDF

Submission history

[v1] 2024-07-09 07:47:06
[v2] 2024-09-20 08:45:15

Unique-IP document downloads: 405 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus