{"id":748969,"date":"2020-01-15T20:27:14","date_gmt":"2020-01-16T04:27:14","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=748969"},"modified":"2021-05-26T20:27:59","modified_gmt":"2021-05-27T03:27:59","slug":"exploring-reinforcement-learning-methods-from-algorithm-to-application","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/exploring-reinforcement-learning-methods-from-algorithm-to-application\/","title":{"rendered":"Exploring Reinforcement Learning Methods from Algorithm to Application"},"content":{"rendered":"

Reinforcement learning (RL) is a systematic approach to learning and decision making under uncertainty. Developed and studied for decades, recent combinations of RL with modern deep learning have led to impressive demonstrations of the capabilities of today’s RL systems, and these new combinations have fueled an explosion of interest and research activity.<\/p>\n

In this webinar led by Microsoft researcher Dr. Katja Hofmann, a Principal Researcher in the Game Intelligence group at Microsoft Research Cambridge, learn about the foundations of RL\u2014elegant ideas giving rise to agents that can learn extremely complex behaviors in a wide range of settings. In the broader perspective, gain an overview of where we currently stand in terms of what is possible in RL from the researcher’s perspective. The webinar concludes with an outlook on key opportunities\u2014both for future research and real-world applications of RL.<\/p>\n

Together, you’ll explore:<\/p>\n