Real World Reinforcement Learning

4월 18, 2023 | Nagarajan Natarajan, Lei Zhao, Rodrigo Fonseca, Tatiana Racheva, 그리고 Yogesh Bansal

In the first two blog posts in this series, we presented our vision for Cloud Intelligence/AIOps (AIOps) research, and scenarios where innovations in AI technologies can help build and operate complex cloud platforms and services effectively and efficiently at scale.…

2월 22, 2023

Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Many real-world applications require sequential decision making, where an agent interacts with…

12월 7, 2020

As human beings, we encounter unfamiliar situations all the time—learning to drive, living on our own for the first time, starting a new job. And while we can anticipate what to expect based on what others have told us or…

12월 7, 2020

Reinforcement learning is about agents taking information from the world and learning a policy for interacting with it, so that they perform better. So, you can imagine a future where, every time you type on the keyboard, the keyboard learns…

6월 3, 2020

MSR’s New York City lab is home to some of the best reinforcement learning research on the planet but if you ask any of the researchers, they’ll tell you they’re very interested in getting it out of the lab and…

6월 3, 2019 | Akshay Krishnamurthy

Reinforcement learning, a machine learning paradigm for sequential decision making, has stormed into the limelight, receiving tremendous attention from both researchers and practitioners. When combined with deep learning, reinforcement learning (RL) has produced impressive empirical results, but the successes to…