14 citations · 40 across the 9 of their papers we have counts for
12 papers
Few-shot Subgoal Planning with Language Models
Lajanugen Logeswaran, Yao Fu, Moontae Lee +1
Pre-trained large language models have shown successful progress in many language understanding benchmarks. This work explores the capability of these models to predict actionable…
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization
Sungryull Sohn, Hyunjae Woo, Jongwook Choi +4
We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is charact…
Learning Parameterized Task Structure for Generalization to Unseen Entities
Anthony Z. Liu, Sungryull Sohn, Mahdi Qazwini +1
Real world tasks are hierarchical and compositional. Tasks can be composed of multiple subtasks (or sub-goals) that are dependent on each other. These subtasks are defined in terms…
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
Jongjin Park, Younggyo Seo, Jinwoo Shin +3
Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the rewar…
Lipschitz-constrained Unsupervised Skill Discovery
Seohong Park, Jongwook Choi, Jaekyeom Kim +2
We study the problem of unsupervised skill discovery, whose goal is to learn a set of diverse and useful skills with no external reward. There have been a number of skill discovery…
Environment Generation for Zero-Shot Compositional Reinforcement Learning
Izzeddin Gur, Natasha Jaques, Yingjie Miao +4
Many real-world problems are compositional - solving them requires completing interdependent sub-tasks, either in series or in parallel, that can be represented as a dependency gra…