{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"TalkRL: The Reinforcement Learning Podcast","title":"RLC 2024 - Posters and Hallways 2","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/d257bea6\"></iframe>","width":"100%","height":180,"duration":952,"description":"Posters and Hallway episodes are short interviews and poster summaries.  Recorded at RLC 2024 in Amherst MA.  Featuring:  0:01 Hector Kohler from Centre Inria de l'Université de Lille with \"Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning\"  2:29 Quentin Delfosse from TU Darmstadt on \"Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents\"  4:15 Sonja Johnson-Yu from Harvard on \"Understanding biological active sensing behaviors by interpreting learned artificial agent policies\"  6:42 Jannis Blüml from TU Darmstadt on \"OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments\"  8:20 Cameron Allen from UC Berkeley on \"Resolving Partial Observability in Decision Processes via the Lambda Discrepancy\"  9:48 James Staley from Tufts on \"Agent-Centric Human Demonstrations Train World Models\"  14:54 Jonathan Li from Rensselaer Polytechnic Institute  ","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}