research
∙
08/17/2023
Reinforced Self-Training (ReST) for Language Modeling
Reinforcement learning from human feedback (RLHF) can improve the qualit...
share
research
∙
08/07/2023
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
StarCraft II is one of the most challenging simulated reinforcement lear...
research
∙
05/05/2023
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning
Standard approaches to sequential decision-making exploit an agent's abi...
research
∙
03/13/2023
Vision-Language Models as Success Detectors
Detecting successful behaviour is crucial for training intelligent agent...
research
∙
10/10/2022
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer
As machine learning permeates more industries and models become more exp...
research
∙
05/26/2022
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior ex...
research
∙
05/12/2022
A Generalist Agent
Inspired by progress in large-scale language modeling, we apply a simila...
research
∙
02/08/2022
Competition-Level Code Generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool. Developin...
research
∙
10/20/2021
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machi...
research
∙
06/18/2021
Active Offline Policy Selection
This paper addresses the problem of policy selection in domains with abu...
research
∙
05/21/2021
On Instrumental Variable Regression for Deep Offline Policy Evaluation
We show that the popular reinforcement learning (RL) strategy of estimat...
research
∙
03/17/2021
Regularized Behavior Value Estimation
Offline reinforcement learning restricts the learning process to rely on...
research
∙
12/12/2020
Semi-supervised reward learning for offline reinforcement learning
In offline reinforcement learning (RL) agents are trained using a logged...
research
∙
11/27/2020
Offline Learning from Demonstrations and Unlabeled Experience
Behavior cloning (BC) is often practical for robot learning because it a...
research
∙
11/06/2020
Large-scale multilingual audio visual dubbing
We describe a system for large-scale audiovisual translation and dubbing...
research
∙
10/14/2020
Learning Deep Features in Instrumental Variable Regression
Instrumental variable (IV) regression is a standard strategy for learnin...
research
∙
07/27/2020
Learning Compositional Neural Programs for Continuous Control
We propose a novel solution to challenging sparse-reward, continuous con...
research
∙
07/17/2020
Hyperparameter Selection for Offline Reinforcement Learning
Offline reinforcement learning (RL purely from logged data) is an import...
research
∙
06/26/2020
Critic Regularized Regression
Offline reinforcement learning (RL), also known as batch RL, offers the ...
research
∙
06/24/2020
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
research
∙
06/01/2020
Acme: A Research Framework for Distributed Reinforcement Learning
Deep reinforcement learning has led to many recent-and groundbreaking-ad...
research
∙
10/02/2019
Task-Relevant Adversarial Imitation Learning
We show that a critical problem in adversarial imitation from high-dimen...
research
∙
09/26/2019
A Framework for Data-Driven Robotics
We present a framework for data-driven robotics that makes use of a larg...
research
∙
09/12/2019
Modular Meta-Learning with Shrinkage
Most gradient-based approaches to meta-learning do not explicitly accoun...
research
∙
09/03/2019
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonst...
research
∙
05/30/2019
Learning Compositional Neural Programs with Recursive Tree Search and Planning
We propose a novel reinforcement learning algorithm, AlphaNPI, that inco...
research
∙
05/08/2019
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for buildi...
research
∙
12/17/2018
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyper-parameters were tuned ...
research
∙
10/19/2018
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communicat...
research
∙
10/19/2018
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
We derive a new intrinsic social motivation for multi-agent reinforcemen...
research
∙
10/11/2018
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
Humans are experts at high-fidelity imitation -- closely mimicking a dem...
research
∙
09/27/2018
Sample Efficient Adaptive Text-to-Speech
We present a meta-learning approach for adaptive text-to-speech (TTS) wi...
research
∙
07/13/2018
Large-Scale Visual Speech Recognition
This work presents a scalable solution to open-vocabulary visual speech ...
research
∙
05/29/2018
Playing hard exploration games by watching YouTube
Deep reinforcement learning methods traditionally struggle with tasks wh...
research
∙
05/24/2018
Hyperbolic Attention Networks
We introduce hyperbolic attention networks to endow neural networks with...
research
∙
04/17/2018
Learning Awareness Models
We consider the setting of an agent with a fixed body interacting with a...
research
∙
04/06/2018
Compositional Obverter Communication Learning From Raw Visual Input
One of the distinguishing aspects of human language is its compositional...
research
∙
02/26/2018
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
We propose a model-free deep reinforcement learning method that leverage...
research
∙
11/07/2017
Cortical microcircuits as gated-recurrent neural networks
Cortical circuits exhibit intricate recurrent architectures that are rem...
research
∙
10/27/2017
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in de...
research
∙
07/11/2017
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
This paper introduces the Intentional Unintentional (IU) agent. This age...
research
∙
06/20/2017
Programmable Agents
We build deep RL agents that execute declarative programs expressed in f...
research
∙
03/14/2017
Learned Optimizers that Scale and Generalize
Learning to learn has emerged as an important direction for achieving ar...
research
∙
03/10/2017
Parallel Multiscale Autoregressive Density Estimation
PixelCNN achieves state-of-the-art results in density estimation for nat...
research
∙
11/11/2016
Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic...
research
∙
11/06/2016
Learning to Perform Physics Experiments via Deep Reinforcement Learning
When encountering novel objects, humans are able to infer a wide range o...
research
∙
11/05/2016
LipNet: End-to-End Sentence-level Lipreading
Lipreading is the task of decoding text from the movement of a speaker's...
research
∙
06/14/2016
Learning to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine lear...
research
∙
02/08/2016
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
We propose deep distributed recurrent Q-networks (DDRQN), which enable t...
research
∙
11/19/2015