#OPENBOX - OPEN ISSUES IN OFFLINE REINFORCEMENT LEARNING - 1/2

OPENBOX aims at bringing an easier understanding of open problems that helps in finding solutions for such problems. For the said purpose, I interview researchers and practitioners who have published works on open problems in various areas of Artificial Intelligence and Machine Learning to collect a simplified understanding of these open problems.  In this episode, Rafael Figueiredo Prudencio discusses open issues in Offline Reinforcement Learning. He covers aspects relating to (a) Off Policy evaluation methods and (b) the lack of adequate real-world benchmarks. Conversation with Rafael is 2 part podcast series, and this podcast is part 1. Listen to the podcast to understand specific ethical issues arising from the open issues. This project is in collaboration with ForHumanity. ForHumanity is a 501(c)(3) nonprofit organization with a mission to minimize the downside risks of AI and autonomous systems. ForHumanity develops criteria for an independent audit of AI systems. To know more, visit https://forhumanity.center/. --- Send in a voice message: https://podcasters.spotify.com/pod/show/ryan-carrier3/message