1.7k citations · 2.4k across the 18 of their papers we have counts for
7 papers · 1 filter
Distilling Internet-Scale Vision-Language Models into Embodied Agents
Theodore Sumers, Kenneth Marino, Arun Ahuja +2
Instruction-following agents must ground language into their observation and action spaces. Learning to ground language is challenging, typically requiring domain-specific engineer…
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
Denis Yarats, Rob Fergus, Alessandro Lazaric +1
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmen…
Empirically Verifying Hypotheses Using Reinforcement Learning
Kenneth Marino, Rob Fergus, Arthur Szlam +1
This paper formulates hypothesis verification as an RL problem. Specifically, we aim to build an agent that, given a hypothesis about the dynamics of the world, can take actions to…
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…
IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning
Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard +4
In order to reach human performance on complexvisual tasks, artificial systems need to incorporate a sig-nificant amount of understanding of the world in termsof macroscopic object…
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…