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Demis Hassabis

9 accepted papers

2021

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents

NeurIPS 2021poster

There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning. One problem in this area of research, however, has been a scarcity of adequate benchmark tasks. In general, the structure underlying past benchmarks ha…

Cited by 38SourcecodeScholar
2020

MEMO: A Deep Network for Flexible Combination of Episodic Memories

ICLR 2020poster

Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning. Here we employed a classic associative inference task from the human neuroscience literatur…

Cited by 0SourceScholar
2018

Memory-based Parameter Adaptation

ICLR 2018poster

Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, an…

Cited by 121SourcePDFScholar
2018

Noisy Networks For Exploration

ICLR 2018poster

We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent’s policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining networ…

Cited by 1259SourcePDFScholar
2018

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

ICML 2018oral

The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is…

Cited by 1053SourcePDFScholar
2018

SCAN: Learning Hierarchical Compositional Visual Concepts

ICLR 2018poster

The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract…

Cited by 151SourcePDFScholar
2017

Imagination-Augmented Agents for Deep Reinforcement Learning

NeurIPS 2017oral

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a polic…

Cited by 763SourcePDFScholar
2017

Neural Episodic Control

ICML 2017poster

Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent…

Cited by 448SourcePDFScholar
2016

The Forget-me-not Process

NeurIPS 2016poster

We introduce the Forget-me-not Process, an efficient, non-parametric meta-algorithm for online probabilistic sequence prediction for piecewise stationary, repeating sources. Our method works by taking a Bayesian approach to partition a stream of data into postulated task-specific segments, while sim…

Cited by 31SourcePDFScholar