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Understanding Incremental Unsupervised Exploration for Goal-based RL | Alessandro Lazaric

ICARL Seminar Series - 2023 Spring Understanding Incremental Unsupervised Exploration for Goal-based RL Seminar by Alessandro Lazaric Abstract: One of the key features of intelligent beings is the capacity to explore and discovery an unknown environment and to progressively learn how to control it. This process is not driven by an explicit reward and it may unfold in a completely unsupervised way. In this talk I will propose a formalization of unsupervised discovery and exploration as the process of incrementally learning policies that reach goals of increasing difficulty. The resulting goal-based policy then allows the agent to solve any goal-reaching task at downstream time with no additional learning or planning. I will illustrate algorithmic principles, theoretical guarantees, and preliminary empirical results that could lay the foundations for designing agents that can efficiently learn in open-ended environments. References: On unsupervised exploration: Adaptive Multi-Goal Exploration; AISTATS 2022 Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching; ICLR 2022 A Provably Efficient Sample Collection Strategy for Reinforcement Learning; NeurIPS 2021 Improved Sample Complexity for Incremental Autonomous Exploration in MDPs; NeurIPS 2020 On exploration for goal-based RL: Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret; NeurIPS 2021 Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model; ALT 2023 —————————————————— Links Alessandro Lazaric Twitter: twitter.com/alelazaric ICARL Site: icarl.doc.ic.ac.uk Twitter: twitter.com/ic_arl YouTube:    / icarlseminars   —————————————————— Intro and Outro music courtesy of Bensound.com - Funky Suspense by Benjamin Tissot