#OPENBOX - OPEN ISSUES IN APPLYING DEEP REINFORCEMENT LEARNING IN COMMUNICATION NETWORKS - 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. Today, we have with us paul. Paul is a PhD Student at Barcelona Neural Networking Center Technical University of Catalunya working on the use of ML to solve problems in communication networks. We are going to cover a paper titled “Towards Real-Time Routing Optimization with Deep Reinforcement Learning: Open Challenges ” published recently which he co-authored. In this podcast, he is covering aspects of (a) Generalization in Deep Reinforcement Learning and (b) Defining an appropriate action space. This is part 1 of the podcast 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