Tim Rocktäschel

research

∙ 08/21/2023

Stabilizing Unsupervised Environment Design with a Learned Adversary

A key challenge in training generally-capable agents is the design of tr...

0 Ishita Mediratta, et al. ∙

research

∙ 03/06/2023

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Open-ended learning methods that automatically generate a curriculum of ...

0 Mikayel Samvelyan, et al. ∙

research

∙ 01/18/2023

Human-Timescale Adaptation in an Open-Ended Task Space

Foundation models have shown impressive adaptation and scalability in su...

0 Adaptive Agent Team, et al. ∙

research

∙ 11/15/2022

General Intelligence Requires Rethinking Exploration

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

0 Minqi Jiang, et al. ∙

research

∙ 11/01/2022

Dungeons and Data: A Large-Scale NetHack Dataset

Recent breakthroughs in the development of agents to solve challenging s...

0 Eric Hambro, et al. ∙

research

∙ 10/26/2022

Large language models are not zero-shot communicators

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

3 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...

0 Yingchen Xu, et al. ∙

research

∙ 10/11/2022

Exploration via Elliptical Episodic Bonuses

In recent years, a number of reinforcement learning (RL) methods have be...

0 Mikael Henaff, et al. ∙

research

∙ 09/30/2022

Improving Policy Learning via Language Dynamics Distillation

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

0 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...

4 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...

0 Michael Matthews, et al. ∙

research

∙ 07/13/2022

GriddlyJS: A Web IDE for Reinforcement Learning

Progress in reinforcement learning (RL) research is often driven by the ...

7 Christopher Bamford, et al. ∙

research

∙ 07/11/2022

Grounding Aleatoric Uncertainty in Unsupervised Environment Design

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

0 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...

0 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...

9 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...

0 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...

5 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 ...

58 Robert Kirk, et al. ∙

research

∙ 10/06/2021

Replay-Guided Adversarial Environment Design

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

0 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...

7 Mikayel Samvelyan, et al. ∙

research

∙ 07/26/2021

Don't Sweep your Learning Rate under the Rug: A Closer Look at Cross-modal Transfer of Pretrained Transformers

Self-supervised pre-training of large-scale transformer models on text c...

3 Danielle Rothermel, et al. ∙

research

∙ 02/08/2021

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning

Although reinforcement learning has been successfully applied in many do...

0 Zhengyao Jiang, et al. ∙

research

∙ 10/08/2020

Prioritized Level Replay

Simulated environments with procedurally generated content have become p...

5 Minqi Jiang, et al. ∙

research

∙ 10/05/2020

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

Multitask Reinforcement Learning is a promising way to obtain models wit...

7 Vitaly Kurin, et al. ∙

research

∙ 10/01/2020

How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds

We seek to create agents that both act and communicate with other agents...

21 Prithviraj Ammanabrolu, et al. ∙

research

∙ 09/04/2020

KILT: a Benchmark for Knowledge Intensive Language Tasks

Challenging problems such as open-domain question answering, fact checki...

6 Fabio Petroni, et al. ∙

research

∙ 07/17/2020

WordCraft: An Environment for Benchmarking Commonsense Agents

The ability to quickly solve a wide range of real-world tasks requires a...

17 Minqi Jiang, 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...

42 Pasquale Minervini, et al. ∙

research

∙ 06/24/2020

The NetHack Learning Environment

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

33 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 ...

22 Andres Campero, et al. ∙

research

∙ 05/22/2020

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Large pre-trained language models have been shown to store factual knowl...

0 Patrick Lewis, et al. ∙

research

∙ 05/10/2020

How Context Affects Language Models' Factual Predictions

When pre-trained on large unsupervised textual corpora, language models ...

0 Fabio Petroni, et al. ∙

research

∙ 04/16/2020

There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training

Natural Language Inference (NLI) datasets contain annotation artefacts r...

8 Joe Stacey, et al. ∙

research

∙ 02/27/2020

RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments

Exploration in sparse reward environments remains one of the key challen...

0 Roberta Raileanu, 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...

1 Pasquale Minervini, et al. ∙

research

∙ 11/20/2019

Generating Interactive Worlds with Text

Procedurally generating cohesive and interesting game environments is ch...

29 Angela Fan, et al. ∙

research

∙ 11/09/2019

How Decoding Strategies Affect the Verifiability of Generated Text

Language models are of considerable importance. They are used for pretra...

0 Luca Massarelli, et al. ∙

research

∙ 10/18/2019

RTFM: Generalising to Novel Environment Dynamics via Reading

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

19 Victor Zhong, et al. ∙

research

∙ 10/09/2019

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions

Effective network congestion control strategies are key to keeping the I...

16 Viswanath Sivakumar, et al. ∙

research

∙ 10/08/2019

TorchBeast: A PyTorch Platform for Distributed RL

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

26 Heinrich Küttler, et al. ∙

research

∙ 09/03/2019

Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora ...

0 Fabio Petroni, et al. ∙

research

∙ 06/14/2019

NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language

Rule-based models are attractive for various tasks because they inherent...

0 Leon Weber, et al. ∙

research

∙ 06/12/2019

Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings

Recent advances in Neural Variational Inference allowed for a renaissanc...

2 Alexander I. Cowen-Rivers, 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 ...

52 Jelena Luketina, et al. ∙

research

∙ 03/07/2019

Learning to Speak and Act in a Fantasy Text Adventure Game

We introduce a large scale crowdsourced text adventure game as a researc...

0 Jack Urbanek, et al. ∙

research

∙ 12/04/2018

e-SNLI: Natural Language Inference with Natural Language Explanations

In order for machine learning to garner widespread public adoption, mode...

0 Oana-Maria Camburu, et al. ∙

research

∙ 12/04/2018

e-SNLI: Natural Language Inference withNatural Language Explanations

In order for machine learning to garner widespread public adoption, mode...

0 Oana-Maria Camburu, et al. ∙

research

∙ 11/20/2018

Stable Opponent Shaping in Differentiable Games

A growing number of learning methods are actually games which optimise m...

74 Alistair Letcher, et al. ∙

research

∙ 08/28/2018

Interpretation of Natural Language Rules in Conversational Machine Reading

Most work in machine reading focuses on question answering problems wher...

0 Marzieh Saeidi, et al. ∙

research

∙ 07/21/2018

Towards Neural Theorem Proving at Scale

Neural models combining representation learning and reasoning in an end-...

0 Pasquale Minervini, et al. ∙

Tim Rocktäschel

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