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20152026
most citedHierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

822 citations · 2.9k across the 43 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.CL2021★ 2 cited

SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark

Victor Zhong, Austin W. Hanjie, Sida I. Wang +2

Existing work in language grounding typically study single environments. How do we build unified models that apply across multiple environments? We propose the multi-environment Sy…

cs.DC2021★ 2 cited

Revelio: ML-Generated Debugging Queries for Distributed Systems

Pradeep Dogga, Karthik Narasimhan, Anirudh Sivaraman +3

A major difficulty in debugging distributed systems lies in manually determining which of the many available debugging tools to use and how to query its logs. Our own study of a pr…

cs.CL2021★ 1 cited

Self-Attention Networks Can Process Bounded Hierarchical Languages

Shunyu Yao, Binghui Peng, Christos Papadimitriou +1

Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as $\maths…

cs.CL2021★ 3 cited

Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

Shunyu Yao, Karthik Narasimhan, Matthew Hausknecht

Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the mot…

cs.CL2021★ 15 cited

Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

Austin W. Hanjie, Victor Zhong, Karthik Narasimhan

We investigate the use of natural language to drive the generalization of control policies and introduce the new multi-task environment Messenger with free-form text manuals descri…