75 citations · 326 across the 20 of their papers we have counts for
8 papers · 1 filter
Hierarchical reinforcement learning with natural language subgoals
Arun Ahuja, Kavya Kopparapu, Rob Fergus +1
Hierarchical reinforcement learning has been a compelling approach for achieving goal directed behavior over long sequences of actions. However, it has been challenging to implemen…
Passive learning of active causal strategies in agents and language models
Andrew Kyle Lampinen, Stephanie C Y Chan, Ishita Dasgupta +2
What can be learned about causality and experimentation from passive data? This question is salient given recent successes of passively-trained language models in interactive domai…
Collaborating with language models for embodied reasoning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino +4
Reasoning in a complex and ambiguous environment is a key goal for Reinforcement Learning (RL) agents. While some sophisticated RL agents can successfully solve difficult tasks, th…
Learning to Navigate Wikipedia by Taking Random Walks
Manzil Zaheer, Kenneth Marino, Will Grathwohl +7
A fundamental ability of an intelligent web-based agent is seeking out and acquiring new information. Internet search engines reliably find the correct vicinity but the top results…
Tell me why! Explanations support learning relational and causal structure
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta +8
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of e…
Distinguishing rule- and exemplar-based generalization in learning systems
Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
Machine learning systems often do not share the same inductive biases as humans and, as a result, extrapolate or generalize in ways that are inconsistent with our expectations. The…
Meta-Learning of Structured Task Distributions in Humans and Machines
Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen +2
In recent years, meta-learning, in which a model is trained on a family of tasks (i.e. a task distribution), has emerged as an approach to training neural networks to perform tasks…
Causal Reasoning from Meta-reinforcement Learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa +7
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether causal reasoning can emerge via meta-reinf…