114 citations · 167 across the 16 of their papers we have counts for
3 papers · 2 filters
Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models
Cong Lu, Shengran Hu, Jeff Clune
Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems built on the principle of archiving discovered states, and iteratively returning to and ex…
Policy-Guided Diffusion
Matthew Thomas Jackson, Michael Tryfan Matthews, Cong Lu +3
In many real-world settings, agents must learn from an offline dataset gathered by some prior behavior policy. Such a setting naturally leads to distribution shift between the beha…
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
Anya Sims, Cong Lu, Jakob Foerster +1
Offline reinforcement learning aims to train agents from pre-collected datasets. However, this comes with the added challenge of estimating the value of behaviors not covered in th…