Artificial Intelligence

   

Before We Can Find a Model, We Must Forget About Perfection

Authors: Dimiter Dobrev

In Reinforcement Learning we look for a model of the world. Typically, we aim to find a model which tells everything or almost everything. In other words, we hunt for a perfect model (a total determinate graph) or for an exhaustive model (Markov Decision Process). Finding such a model is an overly ambitious task and indeed a practically unsolvable problem with complex worlds. In order to solve the problem, we will replace perfect and exhaustive models with Event-Driven models.

Comments: 1 Page. This is a short summary.

Download: PDF

Submission history

[v1] 2019-02-15 04:35:12

Unique-IP document downloads: 0 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