3.7k citations · 11.8k across the 85 of their papers we have counts for
7 papers · 1 filter
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
Sainbayar Sukhbaatar, Emily Denton, Arthur Szlam +1
In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies. We propose an unsupervised learning scheme, based on asymmetric self-play fr…
Dialogue Natural Language Inference
Sean Welleck, Jason Weston, Arthur Szlam +1
Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natur…
Planning with Arithmetic and Geometric Attributes
David Folqué, Sainbayar Sukhbaatar, Arthur Szlam +1
A desirable property of an intelligent agent is its ability to understand its environment to quickly generalize to novel tasks and compose simpler tasks into more complex ones. If…
Lightweight Adaptive Mixture of Neural and N-gram Language Models
Anton Bakhtin, Arthur Szlam, Marc'Aurelio Ranzato +1
It is often the case that the best performing language model is an ensemble of a neural language model with n-grams. In this work, we propose a method to improve how these two mode…
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Roberta Raileanu, Emily Denton, Arthur Szlam +1
We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility. The reward function depends on the…
Composable Planning with Attributes
Amy Zhang, Adam Lerer, Sainbayar Sukhbaatar +2
The tasks that an agent will need to solve often are not known during training. However, if the agent knows which properties of the environment are important then, after learning h…
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek +3
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the…