80 citations · 96 across the 3 of their papers we have counts for
3 papers
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…
Projection-Based Constrained Policy Optimization
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan +1
We consider the problem of learning control policies that optimize a reward function while satisfying constraints due to considerations of safety, fairness, or other costs. We prop…
Robust and Interpretable Grounding of Spatial References with Relation Networks
Tsung-Yen Yang, Andrew S. Lan, Karthik Narasimhan
Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation. Recent work has investigated var…