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03/30/2023
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions
Offline reinforcement learning (RL) allows for the training of competent...
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03/24/2023
Optimal Transport for Offline Imitation Learning
With the advent of large datasets, offline reinforcement learning (RL) i...
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11/15/2022
General Intelligence Requires Rethinking Exploration
We are at the cusp of a transition from "learning from data" to "learnin...
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10/26/2022
Large language models are not zero-shot communicators
Despite widespread use of LLMs as conversational agents, evaluations of ...
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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...
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09/30/2022
Improving Policy Learning via Language Dynamics Distillation
Recent work has shown that augmenting environments with language descrip...
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08/22/2022
Efficient Planning in a Compact Latent Action Space
While planning-based sequence modelling methods have shown great potenti...
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07/23/2022
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning
Practising and honing skills forms a fundamental component of how humans...
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07/11/2022
Grounding Aleatoric Uncertainty in Unsupervised Environment Design
Adaptive curricula in reinforcement learning (RL) have proven effective ...
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05/31/2022
Graph Backup: Data Efficient Backup Exploiting Markovian Transitions
The successes of deep Reinforcement Learning (RL) are limited to setting...
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03/22/2022
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 N...
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03/02/2022
Evolving Curricula with Regret-Based Environment Design
It remains a significant challenge to train generally capable agents wit...
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02/17/2022
Improving Intrinsic Exploration with Language Abstractions
Reinforcement learning (RL) agents are particularly hard to train when r...
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11/18/2021
A Survey of Generalisation in Deep Reinforcement Learning
The study of generalisation in deep Reinforcement Learning (RL) aims to ...
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10/06/2021
Replay-Guided Adversarial Environment Design
Deep reinforcement learning (RL) agents may successfully generalize to n...
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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...
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07/13/2020
Learning Reasoning Strategies in End-to-End Differentiable Proving
Attempts to render deep learning models interpretable, data-efficient, a...
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06/24/2020
The NetHack Learning Environment
Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand wit...
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06/22/2020
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in ...
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12/17/2019
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Reasoning with knowledge expressed in natural language and Knowledge Bas...
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10/18/2019
RTFM: Generalising to Novel Environment Dynamics via Reading
Obtaining policies that can generalise to new environments in reinforcem...
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10/08/2019
TorchBeast: A PyTorch Platform for Distributed RL
TorchBeast is a platform for reinforcement learning (RL) research in PyT...
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10/03/2019
Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep...
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06/10/2019
A Survey of Reinforcement Learning Informed by Natural Language
To be successful in real-world tasks, Reinforcement Learning (RL) needs ...
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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...
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04/02/2019
Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoning---a core ability within human intelligence---pres...
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12/04/2018
Compositional Imitation Learning: Explaining and executing one task at a time
We introduce a framework for Compositional Imitation Learning and Execut...
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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...
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06/05/2018
Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcement-learning agents can learn ...
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02/23/2018
Can Neural Networks Understand Logical Entailment?
We introduce a new dataset of logical entailments for the purpose of mea...
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12/19/2017
The NarrativeQA Reading Comprehension Challenge
Reading comprehension (RC)---in contrast to information retrieval---requ...
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11/13/2017
Learning Explanatory Rules from Noisy Data
Artificial Neural Networks are powerful function approximators capable o...
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06/01/2017
Discovering Discrete Latent Topics with Neural Variational Inference
Topic models have been widely explored as probabilistic generative model...
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06/01/2017
Learning to Compute Word Embeddings On the Fly
Words in natural language follow a Zipfian distribution whereby some wor...
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11/28/2016
Learning to Compose Words into Sentences with Reinforcement Learning
We use reinforcement learning to learn tree-structured neural networks f...
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11/08/2016
The Neural Noisy Channel
We formulate sequence to sequence transduction as a noisy channel decodi...
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09/29/2016
Semantic Parsing with Semi-Supervised Sequential Autoencoders
We present a novel semi-supervised approach for sequence transduction an...
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03/22/2016
Latent Predictor Networks for Code Generation
Many language generation tasks require the production of text conditione...
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09/22/2015
Reasoning about Entailment with Neural Attention
While most approaches to automatically recognizing entailment relations ...
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06/10/2015
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive ...
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06/08/2015
Learning to Transduce with Unbounded Memory
Recently, strong results have been demonstrated by Deep Recurrent Neural...
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12/31/2010
