Edward Grefenstette

research

∙ 11/15/2022

General Intelligence Requires Rethinking Exploration

We are at the cusp of a transition from "learning from data" to "learnin...

Minqi Jiang, et al. ∙

research

∙ 10/26/2022

Large language models are not zero-shot communicators

Despite widespread use of LLMs as conversational agents, evaluations of ...

Laura Ruis, et al. ∙

research

∙ 10/23/2022

Learning General World Models in a Handful of Reward-Free Deployments

Building generally capable agents is a grand challenge for deep reinforc...

Yingchen Xu, et al. ∙

research

∙ 09/30/2022

Improving Policy Learning via Language Dynamics Distillation

Recent work has shown that augmenting environments with language descrip...

Victor Zhong, et al. ∙

research

∙ 08/22/2022

Efficient Planning in a Compact Latent Action Space

While planning-based sequence modelling methods have shown great potenti...

Zhengyao Jiang, et al. ∙

research

∙ 07/23/2022

Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning

Practising and honing skills forms a fundamental component of how humans...

Michael Matthews, et al. ∙

research

∙ 07/11/2022

Grounding Aleatoric Uncertainty in Unsupervised Environment Design

Adaptive curricula in reinforcement learning (RL) have proven effective ...

Minqi Jiang, et al. ∙

research

∙ 05/31/2022

Graph Backup: Data Efficient Backup Exploiting Markovian Transitions

The successes of deep Reinforcement Learning (RL) are limited to setting...

Zhengyao Jiang, et al. ∙

research

∙ 03/22/2022

Insights From the NeurIPS 2021 NetHack Challenge

In this report, we summarize the takeaways from the first NeurIPS 2021 N...

Eric Hambro, et al. ∙

research

∙ 03/02/2022

Evolving Curricula with Regret-Based Environment Design

It remains a significant challenge to train generally capable agents wit...

Jack Parker-Holder, et al. ∙

research

∙ 02/17/2022

Improving Intrinsic Exploration with Language Abstractions

Reinforcement learning (RL) agents are particularly hard to train when r...

Jesse Mu, et al. ∙

research

∙ 11/18/2021

A Survey of Generalisation in Deep Reinforcement Learning

The study of generalisation in deep Reinforcement Learning (RL) aims to ...

Robert Kirk, et al. ∙

research

∙ 10/06/2021

Replay-Guided Adversarial Environment Design

Deep reinforcement learning (RL) agents may successfully generalize to n...

Minqi Jiang, et al. ∙

research

∙ 09/27/2021

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

The progress in deep reinforcement learning (RL) is heavily driven by th...

Mikayel Samvelyan, et al. ∙

research

∙ 07/13/2020

Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, a...

Pasquale Minervini, et al. ∙

research

∙ 06/24/2020

The NetHack Learning Environment

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand wit...

Heinrich Küttler, et al. ∙

research

∙ 06/22/2020

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in ...

Andres Campero, et al. ∙

research

∙ 12/17/2019

Differentiable Reasoning on Large Knowledge Bases and Natural Language

Reasoning with knowledge expressed in natural language and Knowledge Bas...

Pasquale Minervini, et al. ∙

research

∙ 10/18/2019

RTFM: Generalising to Novel Environment Dynamics via Reading

Obtaining policies that can generalise to new environments in reinforcem...

Victor Zhong, et al. ∙

research

∙ 10/08/2019

TorchBeast: A PyTorch Platform for Distributed RL

TorchBeast is a platform for reinforcement learning (RL) research in PyT...

Heinrich Küttler, et al. ∙

research

∙ 10/03/2019

Generalized Inner Loop Meta-Learning

Many (but not all) approaches self-qualifying as "meta-learning" in deep...

Edward Grefenstette, et al. ∙

research

∙ 06/10/2019

A Survey of Reinforcement Learning Informed by Natural Language

To be successful in real-world tasks, Reinforcement Learning (RL) needs ...

Jelena Luketina, et al. ∙

research

∙ 04/26/2019

Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications

Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...

Chenglong Wang, et al. ∙

research

∙ 04/02/2019

Analysing Mathematical Reasoning Abilities of Neural Models

Mathematical reasoning---a core ability within human intelligence---pres...

David Saxton, et al. ∙

research

∙ 12/04/2018

Compositional Imitation Learning: Explaining and executing one task at a time

We introduce a framework for Compositional Imitation Learning and Execut...

Thomas Kipf, et al. ∙

research

∙ 11/22/2018

Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles

While deep learning has led to remarkable results on a number of challen...

Edward Grefenstette, et al. ∙

research

∙ 06/05/2018

Learning to Follow Language Instructions with Adversarial Reward Induction

Recent work has shown that deep reinforcement-learning agents can learn ...

Dzmitry Bahdanau, et al. ∙

research

∙ 02/23/2018

Can Neural Networks Understand Logical Entailment?

We introduce a new dataset of logical entailments for the purpose of mea...

Richard Evans, et al. ∙

research

∙ 12/19/2017

The NarrativeQA Reading Comprehension Challenge

Reading comprehension (RC)---in contrast to information retrieval---requ...

Tomáš Kočiský, et al. ∙

research

∙ 11/13/2017

Learning Explanatory Rules from Noisy Data

Artificial Neural Networks are powerful function approximators capable o...

Richard Evans, et al. ∙

research

∙ 06/01/2017

Discovering Discrete Latent Topics with Neural Variational Inference

Topic models have been widely explored as probabilistic generative model...

Yishu Miao, et al. ∙

research

∙ 06/01/2017

Learning to Compute Word Embeddings On the Fly

Words in natural language follow a Zipfian distribution whereby some wor...

Dzmitry Bahdanau, et al. ∙

research

∙ 11/28/2016

Learning to Compose Words into Sentences with Reinforcement Learning

We use reinforcement learning to learn tree-structured neural networks f...

Dani Yogatama, et al. ∙

research

∙ 11/08/2016

The Neural Noisy Channel

We formulate sequence to sequence transduction as a noisy channel decodi...

Lei Yu, et al. ∙

research

∙ 09/29/2016

Semantic Parsing with Semi-Supervised Sequential Autoencoders

We present a novel semi-supervised approach for sequence transduction an...

Tomáš Kočiský, et al. ∙

research

∙ 03/22/2016

Latent Predictor Networks for Code Generation

Many language generation tasks require the production of text conditione...

Wang Ling, et al. ∙

research

∙ 09/22/2015

Reasoning about Entailment with Neural Attention

While most approaches to automatically recognizing entailment relations ...

Tim Rocktäschel, et al. ∙

research

∙ 06/10/2015

Teaching Machines to Read and Comprehend

Teaching machines to read natural language documents remains an elusive ...

Karl Moritz Hermann, et al. ∙

research

∙ 06/08/2015

Learning to Transduce with Unbounded Memory

Recently, strong results have been demonstrated by Deep Recurrent Neural...

Edward Grefenstette, et al. ∙

research

∙ 12/31/2010

Concrete Sentence Spaces for Compositional Distributional Models of Meaning

Coecke, Sadrzadeh, and Clark (arXiv:1003.4394v1 [cs.CL]) developed a com...

Edward Grefenstette, et al. ∙

Edward Grefenstette

Featured Co-authors