Search Results for author: Edward Grefenstette

Found 52 papers, 23 papers with code

Evolving Curricula with Regret-Based Environment Design

1 code implementation2 Mar 2022 Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel

Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex.

Replay-Guided Adversarial Environment Design

no code implementations NeurIPS 2021 Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel

Furthermore, our theory suggests a highly counterintuitive improvement to PLR: by stopping the agent from updating its policy on uncurated levels (training on less data), we can improve the convergence to Nash equilibria.

Grounding Aleatoric Uncertainty in Unsupervised Environment Design

no code implementations29 Sep 2021 Minqi Jiang, Michael D Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Kuttler, Edward Grefenstette, Tim Rocktäschel, Jakob Nicolaus Foerster

In reinforcement learning (RL), adaptive curricula have proven highly effective for learning policies that generalize well under a wide variety of changes to the environment.

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

1 code implementation27 Sep 2021 Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel

By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use.

NetHack reinforcement-learning +1

Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay

no code implementations NeurIPS Workshop ICBINB 2021 Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette

Prioritized Level Replay (PLR) has been shown to induce adaptive curricula that improve the sample-efficiency and generalization of reinforcement learning policies in environments featuring multiple tasks or levels.

reinforcement-learning

Prioritized Level Replay

2 code implementations8 Oct 2020 Minqi Jiang, Edward Grefenstette, Tim Rocktäschel

Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning.

Systematic Generalization

Learning Reasoning Strategies in End-to-End Differentiable Proving

2 code implementations ICML 2020 Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel

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 Theorem Provers (NTPs).

Link Prediction Relational Reasoning

The NetHack Learning Environment

3 code implementations NeurIPS 2020 Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel

Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack.

NetHack Score Systematic Generalization

RTFM: Generalising to New Environment Dynamics via Reading

no code implementations ICLR 2020 Victor Zhong, Tim Rocktäschel, Edward Grefenstette

In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.

Differentiable Reasoning on Large Knowledge Bases and Natural Language

3 code implementations17 Dec 2019 Pasquale Minervini, Matko Bošnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette

Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering.

Link Prediction Question Answering +1

RTFM: Generalising to Novel Environment Dynamics via Reading

2 code implementations18 Oct 2019 Victor Zhong, Tim Rocktäschel, Edward Grefenstette

In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.

Generalized Inner Loop Meta-Learning

3 code implementations3 Oct 2019 Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala

Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.

Meta-Learning reinforcement-learning

Meta Learning via Learned Loss

no code implementations25 Sep 2019 Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier

We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.

Meta-Learning reinforcement-learning

Meta-Learning via Learned Loss

1 code implementation12 Jun 2019 Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier

This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.

Meta-Learning

A Survey of Reinforcement Learning Informed by Natural Language

no code implementations10 Jun 2019 Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel

To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand.

Decision Making Natural Language Understanding +2

Scalable Neural Theorem Proving on Knowledge Bases and Natural Language

no code implementations ICLR 2019 Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Edward Grefenstette, Sebastian Riedel

Reasoning over text and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering.

Automated Theorem Proving Link Prediction +2

Analysing Mathematical Reasoning Abilities of Neural Models

6 code implementations ICLR 2019 David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli

The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes.

Mathematical Reasoning Math Word Problem Solving

CompILE: Compositional Imitation Learning and Execution

3 code implementations4 Dec 2018 Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter Battaglia

We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demonstration data.

Continuous Control Imitation Learning

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

no code implementations ICLR 2019 Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli

We show that increasing the number of parameters in adversarially-trained models increases their robustness, and in particular that ensembling smaller models while adversarially training the entire ensemble as a single model is a more efficient way of spending said budget than simply using a larger single model.

Self-Driving Cars

Learning to Understand Goal Specifications by Modelling Reward

1 code implementation ICLR 2019 Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.

Can Neural Networks Understand Logical Entailment?

no code implementations ICLR 2018 Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette

We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task.

The NarrativeQA Reading Comprehension Challenge

1 code implementation TACL 2018 Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette

Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document.

Ranked #9 on Question Answering on NarrativeQA (BLEU-1 metric)

Information Retrieval Question Answering +1

Learning Explanatory Rules from Noisy Data

2 code implementations13 Nov 2017 Richard Evans, Edward Grefenstette

Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised.

Inductive logic programming

Deep Learning for Semantic Composition

no code implementations ACL 2017 Xiaodan Zhu, Edward Grefenstette

Learning representation to model the meaning of text has been a core problem in NLP.

Semantic Composition

Learning to Compose Words into Sentences with Reinforcement Learning

no code implementations28 Nov 2016 Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling

We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences.

reinforcement-learning

The Neural Noisy Channel

no code implementations8 Nov 2016 Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomas Kocisky

We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models.

Machine Translation Morphological Inflection +1

Reasoning about Entailment with Neural Attention

6 code implementations22 Sep 2015 Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom

We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases.

Natural Language Inference

Learning to Transduce with Unbounded Memory

4 code implementations NeurIPS 2015 Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom

Recently, strong results have been demonstrated by Deep Recurrent Neural Networks on natural language transduction problems.

Natural Language Transduction Translation

Investigating the Role of Prior Disambiguation in Deep-learning Compositional Models of Meaning

no code implementations15 Nov 2014 Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette

This paper aims to explore the effect of prior disambiguation on neural network- based compositional models, with the hope that better semantic representations for text compounds can be produced.

A Deep Architecture for Semantic Parsing

no code implementations WS 2014 Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann

Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries.

Semantic Parsing

Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics

no code implementations6 Nov 2013 Edward Grefenstette

This thesis focuses on a particular approach to this compositionality problem, namely using the categorical framework developed by Coecke, Sadrzadeh, and Clark, which combines syntactic analysis formalisms with distributional semantic representations of meaning to produce syntactically motivated composition operations.

"Not not bad" is not "bad": A distributional account of negation

no code implementations10 Jun 2013 Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom

With the increasing empirical success of distributional models of compositional semantics, it is timely to consider the types of textual logic that such models are capable of capturing.

A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure

no code implementations2 May 2013 Stephen Clark, Bob Coecke, Edward Grefenstette, Stephen Pulman, Mehrnoosh Sadrzadeh

We discuss an algorithm which produces the meaning of a sentence given meanings of its words, and its resemblance to quantum teleportation.

Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors

no code implementations SEMEVAL 2013 Edward Grefenstette

This paper seeks to bring this reconciliation one step further by showing how the mathematical constructs commonly used in compositional distributional models, such as tensors and matrices, can be used to simulate different aspects of predicate logic.

Experimental Support for a Categorical Compositional Distributional Model of Meaning

no code implementations20 Jun 2011 Edward Grefenstette, Mehrnoosh Sadrzadeh

The evaluation is based on the word disambiguation task developed by Mitchell and Lapata (2008) for intransitive sentences, and on a similar new experiment designed for transitive sentences.

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