引用次数
引用: 34,544
H-指数: 43
I10-指数 : 81
出版物: 109
TRAINING MACHINE LEARNING MODELS
De Freitas Joao Ferdinando Gomes , Colmenarejo Sergio Gomez , Denil Misha Man Ray , Andrychowicz Marcin
2
2019
MODULATING AGENT BEHAVIOR TO OPTIMIZE LEARNING PROGRESS
Tom Schaul , Diana Luiza Borsa , Fengning Ding , David Szepesvari
2021
Universal Value Function Approximators
Tom Schaul , Daniel Horgan , David Silver , Karol Gregor
international conference on machine learning 1312 -1320
936
2015
General video game playing
Tom Schaul , Risto Miikkulainen , Simon M. Lucas , Graham Kendall
computational intelligence and games 6 77 -83
63
2013
Artificial curiosity for autonomous space exploration
Vincent Graziano , Tobias Glasmachers , Tom Schaul , Leonard Pape
Acta Futura
11
2011
Q-error as a selection mechanism in modular reinforcement-learning systems
Tom Schaul , Mark Ring
international joint conference on artificial intelligence 1452 -1457
7
2011
Studies in Continuous Black-box Optimization
Tom Schaul
3
2011
Ontogenetic and Phylogenetic Reinforcement Learning
Tom Schaul , Julian Togelius , Daan Wierstra , Christian Igel
Künstliche Intelligenz 23 ( 3) 30 -33
37
2009
Dueling network architectures for deep reinforcement learning
Tom Schaul , Marc Lanctot , Nando De Freitas , Ziyu Wang
international conference on machine learning 1995 -2003
3,451
2016
Unifying Count-Based Exploration and Intrinsic Motivation
Tom Schaul , Remi Munos , David Saxton , Georg Ostrovski
arXiv: Artificial Intelligence
1,259
2016
Reinforcement Learning with Unsupervised Auxiliary Tasks
Tom Schaul , Volodymyr Mnih , Koray Kavukcuoglu , Joel Z Leibo
international conference on learning representations
1,147
2016
General Video Game AI: competition, challenges, and opportunities
Tom Schaul , Spyridon Samothrakis , Julian Togelius , Simon M. Lucas
national conference on artificial intelligence 4335 -4337
201
2016
FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets , Tom Schaul , Koray Kavukcuoglu , Simon Osindero
international conference on machine learning 3540 -3549
798
2017
StarCraft II: A New Challenge for Reinforcement Learning
Sergey Bartunov , Alexander Sasha Vezhnevets , Tom Schaul , Karen Simonyan
arXiv: Learning
814
2017
Natural Value Approximators: Learning when to Trust Past Estimates
David Silver , Andre Barreto , Hado P. van Hasselt , Tom Schaul
neural information processing systems 30 2120 -2128
3
2017
Unicorn: Continual learning with a universal, off-policy agent
Tom Schaul , Daniel J. Mankowitz , Matteo Hessel , Junhyuk Oh
arXiv: Learning
39
2018
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Tom Schaul , Razvan Pascanu , Joseph Modayil , Diana Borsa
arXiv: Learning
53
2019
Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods.
Tom Schaul , Karel Lenc , Karen Simonyan , Erich Elsen
arXiv: Neural and Evolutionary Computing
6
2019
A Linear Time Natural Evolution Strategy for Non-Separable Functions
Tom Schaul , Faustino Gomez , Juergen Schmidhuber , Yi Sun
arXiv: Artificial Intelligence
30
2011
No More Pesky Learning Rates
Tom Schaul , Yann LeCun , Sixin Zhang
arXiv: Machine Learning
503
2012