Web: https://blog.ml.cmu.edu/2022/05/06/barl/

May 6, 2022, 9:36 p.m. | Viraj Mehta

Machine Learning Blog | ML@CMU | Carnegie Mellon University cmu.edu

Reinforcement learning (RL) has achieved astonishing successes in domains where the environment is easy to simulate. For example, in games like Go or those in the Atari library, agents can play millions of games in the course of days to explore the environment and find superhuman policies. However, transfer of these advances to broader real-world applications is challenging because the cost of exploration in many important domains is high.

design experimental learning machine learning model on perspective reinforcement reinforcement learning research

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California