{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"TalkRL: The Reinforcement Learning Podcast","title":"Outstanding Paper Award Winners - 1/2 @ RLC 2025","html":"","width":"100%","height":180,"duration":406,"description":"We caught up with the RLC Outstanding Paper award winners for your listening pleasure. \nRecorded on location at Reinforcement Learning Conference 2025, at University of Alberta, in Edmonton Alberta Canada in August 2025.\nFeatured References \n\nScientific Understanding in Reinforcement Learning \nHow Should We Meta-Learn Reinforcement Learning Algorithms? \nAlexander David Goldie, Zilin Wang, Jakob Nicolaus Foerster, Shimon Whiteson \nTooling, Environments, and Evaluation for Reinforcement Learning \nSyllabus: Portable Curricula for Reinforcement Learning Agents \nRyan Sullivan, Ryan Pégoud, Ameen Ur Rehman, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P Dickerson \nResourcefulness in Reinforcement Learning \nPufferLib 2.0: Reinforcement Learning at 1M steps/s \nJoseph Suarez \nTheory of Reinforcement Learning \nDeep Reinforcement Learning with Gradient  Eligibility Traces  \nEsraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Martha White  ","thumbnail_url":"https://img.transistorcdn.com/jXB1-VPK-A9v1epzc4aG4pFxqlvo2vbQ_Ytyuar_gPI/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzIwNDcvMTcwNzk1/NDcxMS1hcnR3b3Jr/LmpwZw.webp","thumbnail_width":300,"thumbnail_height":300}