Abstracts: NeurIPS 2024 with Dylan Foster
DYLAN FOSTER: Thanks for having me.
TINGLE: Let’s start with a brief overview of this paper. Tell us about the problem this work addresses and why the research community should know about it.
FOSTER: So this is a, kind of, a theoretical work on reinforcement learning, or RL. When I say reinforcement learning, broadly speaking, this is talking about the question of how can we design AI agents that are capable of, like, interacting with unknown environments and learning how to solve problems through trial and error. So this is part of some broader agenda we’ve been doing on, kind of, theoretical foundations of RL. And the key questions we’re looking at here are what are called, like, exploration and sample efficiency. So this just means we’re trying to