27 citations · 102 across the 20 of their papers we have counts for
5 papers · 1 filter
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration
Srivatsan Krishnan, Natasha Jaques, Shayegan Omidshafiei +4
Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fi…
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff +4
Reinforcement Learning (RL) algorithms are often known for sample inefficiency and difficult generalization. Recently, Unsupervised Environment Design (UED) emerged as a new paradi…
Understanding HTML with Large Language Models
Izzeddin Gur, Ofir Nachum, Yingjie Miao +6
Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. Yet, their capabilities for HTML understanding -- i.e., parsing the raw HTML…
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…
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…