David Silver

is this you? claim profile

∙ 0 followers
Google ∙ University of Alberta ∙ UCL

Leads reinforcement learning research group at DeepMind

Bayesian Optimization in AlphaGo

During the development of AlphaGo, its many hyper-parameters were tuned ...
12/17/2018 ∙ by Yutian Chen, et al. ∙ 128 ∙ share

read it
What Can Learned Intrinsic Rewards Capture?

Reinforcement learning agents can include different components, such as ...
12/11/2019 ∙ by Zeyu Zheng, et al. ∙ 25 ∙ share

read it
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Constructing agents with planning capabilities has long been one of the ...
11/19/2019 ∙ by Julian Schrittwieser, et al. ∙ 23 ∙ share

read it
Self-Tuning Deep Reinforcement Learning

Reinforcement learning (RL) algorithms often require expensive manual or...
02/28/2020 ∙ by Tom Zahavy, et al. ∙ 20 ∙ share

read it
Value-driven Hindsight Modelling

Value estimation is a critical component of the reinforcement learning (...
02/19/2020 ∙ by Arthur Guez, et al. ∙ 17 ∙ share

read it
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement

The ability to transfer skills across tasks has the potential to scale u...
01/30/2019 ∙ by Andre Barreto, et al. ∙ 12 ∙ share

read it
An investigation of model-free planning

The field of reinforcement learning (RL) is facing increasingly challeng...
01/11/2019 ∙ by Arthur Guez, et al. ∙ 10 ∙ share

read it
Discovery of Useful Questions as Auxiliary Tasks

Arguably, intelligent agents ought to be able to discover their own ques...
09/10/2019 ∙ by Vivek Veeriah, et al. ∙ 7 ∙ share

read it
Universal Successor Features Approximators

The ability of a reinforcement learning (RL) agent to learn about many r...
12/18/2018 ∙ by Diana Borsa, et al. ∙ 6 ∙ share

read it
On Inductive Biases in Deep Reinforcement Learning

Many deep reinforcement learning algorithms contain inductive biases tha...
07/05/2019 ∙ by Matteo Hessel, et al. ∙ 6 ∙ share

read it
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Recent progress in artificial intelligence through reinforcement learnin...
07/03/2018 ∙ by Max Jaderberg, et al. ∙ 2 ∙ share

read it
Behaviour Suite for Reinforcement Learning

This paper introduces the Behaviour Suite for Reinforcement Learning, or...
08/09/2019 ∙ by Ian Osband, et al. ∙ 2 ∙ share

read it
Playing Atari with Deep Reinforcement Learning

We present the first deep learning model to successfully learn control p...
12/19/2013 ∙ by Volodymyr Mnih, et al. ∙ 0 ∙ share

read it
Asynchronous Methods for Deep Reinforcement Learning

We propose a conceptually simple and lightweight framework for deep rein...
02/04/2016 ∙ by Volodymyr Mnih, et al. ∙ 0 ∙ share

read it
Decoupled Neural Interfaces using Synthetic Gradients

Training directed neural networks typically requires forward-propagating...
08/18/2016 ∙ by Max Jaderberg, et al. ∙ 0 ∙ share

read it
Rainbow: Combining Improvements in Deep Reinforcement Learning

The deep reinforcement learning community has made several independent i...
10/06/2017 ∙ by Matteo Hessel, et al. ∙ 0 ∙ share

read it
StarCraft II: A New Challenge for Reinforcement Learning

This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...
08/16/2017 ∙ by Oriol Vinyals, et al. ∙ 0 ∙ share

read it
Reinforcement Learning with Unsupervised Auxiliary Tasks

Deep reinforcement learning agents have achieved state-of-the-art result...
11/16/2016 ∙ by Max Jaderberg, et al. ∙ 0 ∙ share

read it
Emergence of Locomotion Behaviours in Rich Environments

The reinforcement learning paradigm allows, in principle, for complex be...
07/07/2017 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

read it
Imagination-Augmented Agents for Deep Reinforcement Learning

We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
07/19/2017 ∙ by Theophane Weber, et al. ∙ 0 ∙ share

read it
Learning Continuous Control Policies by Stochastic Value Gradients

We present a unified framework for learning continuous control policies ...
10/30/2015 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

read it
Massively Parallel Methods for Deep Reinforcement Learning

We present the first massively distributed architecture for deep reinfor...
07/15/2015 ∙ by Arun Nair, et al. ∙ 0 ∙ share

read it
Move Evaluation in Go Using Deep Convolutional Neural Networks

The game of Go is more challenging than other board games, due to the di...
12/20/2014 ∙ by Chris J. Maddison, et al. ∙ 0 ∙ share

read it
The Predictron: End-To-End Learning and Planning

One of the key challenges of artificial intelligence is to learn models ...
12/28/2016 ∙ by David Silver, et al. ∙ 0 ∙ share

read it
Learning and Transfer of Modulated Locomotor Controllers

We study a novel architecture and training procedure for locomotion task...
10/17/2016 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

read it
Successor Features for Transfer in Reinforcement Learning

Transfer in reinforcement learning refers to the notion that generalizat...
06/16/2016 ∙ by Andre Barreto, et al. ∙ 0 ∙ share

read it
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

Many real-world applications can be described as large-scale games of im...
03/03/2016 ∙ by Johannes Heinrich, et al. ∙ 0 ∙ share

read it
Learning values across many orders of magnitude

Most learning algorithms are not invariant to the scale of the function ...
02/24/2016 ∙ by Hado van Hasselt, et al. ∙ 0 ∙ share

read it
Learning to Win by Reading Manuals in a Monte-Carlo Framework

Domain knowledge is crucial for effective performance in autonomous cont...
01/18/2014 ∙ by S. R. K. Branavan, et al. ∙ 0 ∙ share

read it
Continuous control with deep reinforcement learning

We adapt the ideas underlying the success of Deep Q-Learning to the cont...
09/09/2015 ∙ by Timothy P. Lillicrap, et al. ∙ 0 ∙ share

read it
Value Iteration with Options and State Aggregation

This paper presents a way of solving Markov Decision Processes that comb...
01/16/2015 ∙ by Kamil Ciosek, et al. ∙ 0 ∙ share

read it
Better Optimism By Bayes: Adaptive Planning with Rich Models

The computational costs of inference and planning have confined Bayesian...
02/09/2014 ∙ by Arthur Guez, et al. ∙ 0 ∙ share

read it
A Monte Carlo AIXI Approximation

This paper introduces a principled approach for the design of a scalable...
09/04/2009 ∙ by Joel Veness, et al. ∙ 0 ∙ share

read it
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Bayesian model-based reinforcement learning is a formally elegant approa...
05/14/2012 ∙ by Arthur Guez, et al. ∙ 0 ∙ share

read it
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

The game of chess is the most widely-studied domain in the history of ar...
12/05/2017 ∙ by David Silver, et al. ∙ 0 ∙ share

read it
Unicorn: Continual Learning with a Universal, Off-policy Agent

Some real-world domains are best characterized as a single task, but for...
02/22/2018 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

read it
Learning to Search with MCTSnets

Planning problems are among the most important and well-studied problems...
02/13/2018 ∙ by Arthur Guez, et al. ∙ 0 ∙ share

read it
Distributed Prioritized Experience Replay

We propose a distributed architecture for deep reinforcement learning at...
03/02/2018 ∙ by Dan Horgan, et al. ∙ 0 ∙ share

read it
Unsupervised Predictive Memory in a Goal-Directed Agent

Animals execute goal-directed behaviours despite the limited range and s...
03/28/2018 ∙ by Greg Wayne, et al. ∙ 0 ∙ share

read it
Meta-Gradient Reinforcement Learning

The goal of reinforcement learning algorithms is to estimate and/or opti...
05/24/2018 ∙ by Zhongwen Xu, et al. ∙ 0 ∙ share

read it
Implicit Quantile Networks for Distributional Reinforcement Learning

In this work, we build on recent advances in distributional reinforcemen...
06/14/2018 ∙ by Will Dabney, et al. ∙ 0 ∙ share

read it
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

To achieve general intelligence, agents must learn how to interact with ...
11/02/2017 ∙ by Marc Lanctot, et al. ∙ 0 ∙ share

read it
Credit Assignment Techniques in Stochastic Computation Graphs

Stochastic computation graphs (SCGs) provide a formalism to represent st...
01/07/2019 ∙ by Theophane Weber, et al. ∙ 0 ∙ share

read it

David Silver

Featured Co-authors