80 citations · 224 across the 11 of their papers we have counts for
30 papers
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
Vitaly Kurin, Maximilian Igl, Tim Rocktäschel +2
Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to comp…
Learning Reasoning Strategies in End-to-End Differentiable Proving
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp +2
Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theor…
WordCraft: An Environment for Benchmarking Commonsense Agents
Minqi Jiang, Jelena Luketina, Nantas Nardelli +4
The ability to quickly solve a wide range of real-world tasks requires a commonsense understanding of the world. Yet, how to best extract such knowledge from natural language corpo…
The NetHack Learning Environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller +4
Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL env…
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
Andres Campero, Roberta Raileanu, Heinrich Küttler +3
A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new…
How Context Affects Language Models' Factual Predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus +4
When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for…