BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Csaba Szepesvári (University of Alberta) DTSTART:20200618T190000Z DTEND:20200618T203000Z DTSTAMP:20260423T003247Z UID:IASML/7 DESCRIPTION:Title: Th e challenges of model-based reinforcement learning and how to overcome the m\nby Csaba Szepesvári (University of Alberta) as part of IAS Seminar Series on Theoretical Machine Learning\n\n\nAbstract\nSome believe that t ruly effective and efficient reinforcement learning algorithms must explic itly construct and explicitly reason with models that capture the causal s tructure of the world. In short\, model-based reinforcement learning is no t optional. As this is not a new belief\, it may be surprising that empiri cally\, at least as far as the current state of art is concerned\, the maj ority of the top performing algorithms are model-free. In this talk\, I wi ll define three major challenges that need to be overcome for model-based methods to take their place above\, or before the model-free ones: (1) pla nning with large models\; (2) models are never well-specified\; (3) models need to focus on task relevant aspects and ignore others. For each of the challenges\, I will describe recent results that address them and I will also take a tally of the most interesting (and challenging) remaining open problems.\n LOCATION:https://researchseminars.org/talk/IASML/7/ END:VEVENT END:VCALENDAR