Search Results for author:
Found 31 papers, 22 papers with code
S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
We present a soft, spatial, sequential, top-down attention model (S3TA).
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain.
Relational recurrent neural networks
Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods.
Ranked #72 on
Language Modelling
on WikiText-103
Relational inductive biases, deep learning, and graph networks
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
Learning to Search with MCTSnets
They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree.
Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models.
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models.
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects.
Deep Reinforcement Learning
Model-based Reinforcement Learning
+2
Learning model-based planning from scratch
Here we introduce the "Imagination-based Planner", the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans.
Comparison of Maximum Likelihood and GAN-based training of Real NVPs
We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN.
Recurrent Environment Simulators
Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently.
Neural Episodic Control
Deep reinforcement learning methods attain super-human performance in a wide range of environments.
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks.
Ranked #5 on
Continual Learning
on F-CelebA (10 tasks)
Variational Intrinsic Control
In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent.
Model-Free Episodic Control
State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance.
Matching Networks for One Shot Learning
Our algorithm improves one-shot accuracy on ImageNet from 87. 6% to 93. 2% and from 88. 0% to 93. 8% on Omniglot compared to competing approaches.
Convolution by Evolution: Differentiable Pattern Producing Networks
In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN.
One-shot Learning with Memory-Augmented Neural Networks
Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of "one-shot learning."
Towards Conceptual Compression
We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling.
One-Shot Generalization in Deep Generative Models
In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept.
Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.
Ranked #2 on
OpenAI Gym
on HalfCheetah-v4
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.
Human level control through deep reinforcement learning
We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters.
Ranked #20 on
Atari Games
on Atari 2600 Ice Hockey
DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.
Deterministic Policy Gradient Algorithms
In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning.
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Ranked #1 on
Atari Games
on Atari 2600 Pong
Deep AutoRegressive Networks
We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data.
Variational Learning for Recurrent Spiking Networks
We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons.
Natural Evolution Strategies
This paper presents Natural Evolution Strategies (NES), a recent family of algorithms that constitute a more principled approach to black-box optimization than established evolutionary algorithms.
