Schlag, KarlZapechelnyuk, AndriyUniversitat Pompeu Fabra. Departament d'Economia i Empresa2017-07-262017-07-262009-06-01http://hdl.handle.net/10230/4795We consider an agent who has to repeatedly make choices in an uncertain and changing environment, who has full information of the past, who discounts future payoffs, but who has no prior. We provide a learning algorithm that performs almost as well as the best of a given finite number of experts or benchmark strategies and does so at any point in time, provided the agent is sufficiently patient. The key is to find the appropriate degree of forgetting distant past. Standard learning algorithms that treat recent and distant past equally do not have the sequential epsilon optimality property.application/pdfengL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commonsinfo:eu-repo/semantics/workingPaperadaptive learningexpertsdistribution-freee-optimalityhannan regretMicroeconomicsStatistics, Econometrics and Quantitative Methodsinfo:eu-repo/semantics/openAccess