{"id":709156,"date":"2020-12-07T07:55:00","date_gmt":"2020-12-07T15:55:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=709156"},"modified":"2021-06-24T14:20:48","modified_gmt":"2021-06-24T21:20:48","slug":"research-collection-reinforcement-learning-at-microsoft","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-collection-reinforcement-learning-at-microsoft\/","title":{"rendered":"Research Collection \u2013 Reinforcement Learning at Microsoft"},"content":{"rendered":"\n

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 to understand you better. Or every time you interact with some website, it understands better what your preferences are, so the world just starts working better and better at interacting with people.<\/em><\/p>John Langford, Partner Research Manager, MSR NYC<\/cite><\/blockquote>\n\n\n\n

Fundamentally, reinforcement learning (RL) is an approach to machine learning in which a software agent interacts with its environment, receives rewards, and chooses actions that will maximize those rewards. Research on reinforcement learning goes back many decades and is rooted in work in many different fields, including animal psychology, and some of its basic concepts were explored in the earliest research on artificial intelligence \u2013 such as Marvin Minsky\u2019s 1951 SNARC machine, which used an ancestor of modern reinforcement learning techniques to simulate a rat solving a maze.<\/p>\n\n\n\n

In the 1990s and 2000s, theoretical and practical work in reinforcement learning began to accelerate, leading to the rapid progress we see today. The theory behind reinforcement learning continues to advance, while its applications in real-world scenarios are leading to meaningful impact in many areas \u2013 from training autonomous systems to operate more safely and reliably in real-world environments, to making games more engaging and entertaining, to delivering more personalized information and experiences on the web.<\/p>\n\n\n\n

Below is a timeline of advances that researchers and their collaborators across Microsoft have made in reinforcement learning, along with key milestones<\/em> in the field generally.<\/p>\n\n\n\n

Foundational work in reinforcement learning (1992-2014)<\/h3>\n\n\n\n