Q-learning is a popular technique in machine learning where an agent learns to make decisions in an environment based on rewards and penalties. However, Q-learning has its limitations in multi-state environments, which is where Deep Q-Learning comes in.
Deep Q-Learning uses neural networks to efficiently solve problems. In this article, we dive deep into the concept of Deep Q-Learning, using the CartPole problem as an example.
In the CartPole problem, an AI must balance a virtual pole on a cart and carry out the best possible action in every situation to prevent the pole from falling over. We explain different aspects of neural networks and how to use them in Deep Q-Learning.
We also describe fixed Q targets and replay buffers, which are important concepts in Deep Q-Learning. This technique is not only interesting for AI developers but also for anyone who wants to understand how machines can develop self-learning systems through trial and error that are capable of handling complex tasks.
It is important to note that this article assumes basic knowledge of Python and machine learning. However, if you need a quick refresher, you can find explanations for many of the technical terms used in our introductory article on Q-Learning.
By the end of this article, you will have a better understanding of how neural networks work and how they can be used to develop self-learning systems. Don’t miss any news in the world of AI and machine learning – sign up for our heise online newsletter today!
