OpenAI trains agents in a simple "hide and seek" game and learns many other skills in the process.
This article summarizes the important knowledge points related to deep learning. It is presented to you through long graphs and PDFs. You are welcome to download PM.
This article is a machine learning science for all, involving all the key knowledge points related to machine learning.
This article shows what DP can bring to some simple but classic control problems, and we usually use reinforcement learning (RL). The DP-based model not only learns more effective control strategies than RL, but also trains orders of magnitude faster.
Reinforcement is a type of machine learning in which agents learn how to behave in the environment by performing actions to draw intuition and see the results. In this article, you will learn how to understand and design reinforcement learning problems and solve them in Python.
If you are the company's decision maker, I hope this article is enough to convince you to rethink your business and see if you can use reinforcement learning.
Compared to other fields, machine learning/artificial intelligence now seems to have a super-funny development with higher frequency.
Little is known about how the brain works, but we know that the brain can learn through repeated attempts. When we make the right choices, we get rewards and we are punished when we make inappropriate choices. This is how we adapt to the environment. Today, we can use powerful computing power to model this specific process in software, which is intensive learning.
Facebook's artificial intelligence research organization FAIR has just opened up and publicly published chat bots have begun to negotiate and negotiate with humans for bargaining power. Through supervised learning + reinforcement learning, this chat robot can not only understand the correspondence between words and semantics, but also formulate strategies for their own goals and reach consensus with others.
In this article, we will help you better understand the implications of the definitions of supervised learning, unsupervised learning, and reinforcement learning, and explain their connection to machine learning from a broader perspective. A deep understanding of their connotations will not only help you to be embarrassed in the literature in this field, but also guide you to sharply capture the development of AI and the advancement of technological progress.