RL is a hugely popular area of deep learning, and many data scientists are exploring this AI technology to broaden their skillet to include a number of important problem domains like chatbots, robotics, discrete optimization, web automation and much more. As a result of this wide-spread interest in RL, there are many available educational resources specifically tailored to this class of deep learning – boot camps, training certificates, educational specializations, etc. But if you’re a data scientist who has been programming in Python for a while, and has some experience with other forms of deep learning using a framework like TensorFlow, then maybe this new book, “Deep Reinforcement Learning Hands-On,” by Maxim Lapan, might be a great way to kick-start yourself into becoming productive with RL.
New Salesforce Research AI Simulates Millions of Years of Economic Data Using RL
Salesforce Research published a groundbreaking paper, The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies, which applies AI and reinforcement learning to create tax policy for the first time.
Tomorrow’s Machine Learning Today: Topological Data Analysis, Embedding, and Reinforcement Learning
In this contributed article, editorial consultant Jelani Harper highlights how certain visual approaches of graph aware systems will significantly shape the form machine learning takes in the near future, exponentially increasing its value to the enterprise. Developments in topological data analysis, embedding, and reinforcement learning are not only rendering this technology more useful, but much more dependable for a broader array of use cases.
Cerebri AI Launches Cerebri Values CX v2 Platform
Cerebri AI, announced the official launch of its second-generation Cerebri Values Customer Experience platform (CV/CX v2), which is now in the hands of customers and in full production. CV/CX v2 insights, brand commitment - Cerebri Values and Next Best Action{set}s are all driven by patent-pending object-oriented AI and reinforcement learning modelling methods. Our technology can time, sequence and value up to four events, rendering ‘rules-based’ and ‘AI-lite’ technology obsolete for driving maximum results.
Deep Reinforcement Learning: From Board Games to the Boardroom
In this contributed article, Andrew Vaziri, a Senior Artificial Intelligence Engineer at Bonsai, highlights how we are now entering the age of profit-making Deep Reinforcement Learning (DRL), and why it took so long for this technology to make the leap from board games to the boardroom?
