{"id":749359,"date":"2019-12-05T13:22:30","date_gmt":"2019-12-05T21:22:30","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=749359"},"modified":"2021-05-27T13:29:12","modified_gmt":"2021-05-27T20:29:12","slug":"foundations-of-real-world-reinforcement-learning","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/foundations-of-real-world-reinforcement-learning\/","title":{"rendered":"Foundations of Real-World Reinforcement Learning"},"content":{"rendered":"

Reinforcement learning (RL) is an approach to sequential decision making under uncertainty which formalizes the principles for designing an autonomous learning agent. The broad goal of a reinforcement learning agent is to find an optimal policy which maximizes its long-term rewards over time. Its list of applications is growing as the technology advances and continues to be further integrated into many areas, such as education, health, advertising, autonomous systems, and gaming.<\/p>\n

By starting from the perspective of an agent which interacts with and affects its environment, RL provides an improvement upon supervised learning in situations requiring decisions, and not just predictions. In particular, it motivates exploratory actions to discover novel rewarding behavior in the environment, a hallmark of intelligent agents.<\/p>\n

In this webinar\u2014led by Microsoft Researchers John Langford, Partner Research Manager with over a decade of experience in reinforcement learning-related research, and Alekh Agarwal, Principal Research Manager and leader of the Reinforcement Learning group in Redmond\u2014learn how RL works to impact real-world problems across a variety of domains.<\/p>\n

Together, you’ll explore:<\/p>\n