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Title: Instance-dependent Reinforcement Learning: A statistical viewpoint
Abstract: In recent years, there has been tremendous progress in the field of reinforcement learning (RL), especially on the empirical side. But it is fair to say that there is a considerable gap between theory and practice: many RL methods behave far better than existing worst-case theory would suggest, and often they work in settings where the current worst-case guarantees are completely prohibitive. In this talk, we will discuss why worst-case guarantees can severely overestimate the difficulty of reinforcement learning problems in presence of favorable structure. This motivates us to consider an instance-dependent difficulty measure that is responsive to the problem structure. Next, we discuss how we can construct estimators that adapt to this instance-dependent difficulty. We show that for problems with favorable structures our proposed estimators and associated confidence regions are significantly better than those obtained from the worst-case theory. Finally, we show that the techniques that we developed for constructing instance-dependent estimators are not specific to RL problems, and can be applied to a broad class of other problems.
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Please be advised that this seminar has been cancelled. We will reschedule the seminar in the near future. We sincerely apologize for any inconvenience.
UBC Statistics Department