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Meta-learning of Sequential Strategies
v1v2 (latest)

Meta-learning of Sequential Strategies

8 May 2019
Pedro A. Ortega
Jane X. Wang
Mark Rowland
Tim Genewein
Z. Kurth-Nelson
Razvan Pascanu
N. Heess
J. Veness
Alex Pritzel
Pablo Sprechmann
Siddhant M. Jayakumar
Tom McGrath
Kevin J. Miller
M. G. Azar
Ian Osband
Neil C. Rabinowitz
András Gyorgy
Silvia Chiappa
Simon Osindero
Yee Whye Teh
H. V. Hasselt
Nando de Freitas
M. Botvinick
Shane Legg
    OffRL
ArXiv (abs)PDFHTML

Papers citing "Meta-learning of Sequential Strategies"

22 / 72 papers shown
Alchemy: A benchmark and analysis toolkit for meta-reinforcement
  learning agents
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
Jane X. Wang
Michael King
Nicolas Porcel
Z. Kurth-Nelson
Tina Zhu
...
Neil C. Rabinowitz
Loic Matthey
Demis Hassabis
Alexander Lerchner
M. Botvinick
OffRL
302
39
0
04 Feb 2021
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
Deep Interactive Bayesian Reinforcement Learning via Meta-LearningAdaptive Agents and Multi-Agent Systems (AAMAS), 2021
L. Zintgraf
Sam Devlin
K. Ciosek
Shimon Whiteson
Katja Hofmann
BDL
175
55
0
11 Jan 2021
Towards Continual Reinforcement Learning: A Review and Perspectives
Towards Continual Reinforcement Learning: A Review and PerspectivesJournal of Artificial Intelligence Research (JAIR), 2020
Khimya Khetarpal
Matthew D Riemer
Irina Rish
Doina Precup
CLLOffRL
537
375
0
25 Dec 2020
Meta-learning in natural and artificial intelligence
Meta-learning in natural and artificial intelligenceCurrent Opinion in Behavioral Sciences (Curr Opin Behav Sci), 2020
Jane X. Wang
152
135
0
26 Nov 2020
Specialization in Hierarchical Learning Systems
Specialization in Hierarchical Learning Systems
Heinke Hihn
Daniel A. Braun
174
18
0
03 Nov 2020
Meta-trained agents implement Bayes-optimal agents
Meta-trained agents implement Bayes-optimal agents
Vladimir Mikulik
Grégoire Delétang
Tom McGrath
Tim Genewein
Miljan Martic
Shane Legg
Pedro A. Ortega
OODFedML
218
45
0
21 Oct 2020
Few-shot model-based adaptation in noisy conditions
Few-shot model-based adaptation in noisy conditionsIEEE Robotics and Automation Letters (RA-L), 2020
Karol Arndt
Ali Ghadirzadeh
Murtaza Hazara
Ville Kyrki
144
9
0
16 Oct 2020
Learning Not to Learn: Nature versus Nurture in Silico
Learning Not to Learn: Nature versus Nurture in SilicoAAAI Conference on Artificial Intelligence (AAAI), 2020
R. T. Lange
Henning Sprekeler
292
10
0
09 Oct 2020
Exploration in Approximate Hyper-State Space for Meta Reinforcement
  Learning
Exploration in Approximate Hyper-State Space for Meta Reinforcement LearningInternational Conference on Machine Learning (ICML), 2020
L. Zintgraf
Leo Feng
Cong Lu
Maximilian Igl
Kristian Hartikainen
Katja Hofmann
Shimon Whiteson
349
43
0
02 Oct 2020
Importance Weighted Policy Learning and Adaptation
Importance Weighted Policy Learning and Adaptation
Alexandre Galashov
Jakub Sygnowski
Guillaume Desjardins
Jan Humplik
Leonard Hasenclever
Rae Jeong
Yee Whye Teh
N. Heess
OffRL
212
1
0
10 Sep 2020
Sparse Meta Networks for Sequential Adaptation and its Application to
  Adaptive Language Modelling
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling
Tsendsuren Munkhdalai
CLLOffRL
189
5
0
03 Sep 2020
Offline Meta Learning of Exploration
Offline Meta Learning of Exploration
Ron Dorfman
Idan Shenfeld
Aviv Tamar
OffRL
234
20
0
06 Aug 2020
Meta-Learning Bandit Policies by Gradient Ascent
Meta-Learning Bandit Policies by Gradient Ascent
Branislav Kveton
Martin Mladenov
Chih-Wei Hsu
Manzil Zaheer
Csaba Szepesvári
Craig Boutilier
177
9
0
09 Jun 2020
The Synthesizability of Molecules Proposed by Generative Models
The Synthesizability of Molecules Proposed by Generative ModelsJournal of Chemical Information and Modeling (JCIM), 2020
Wenhao Gao
Connor W. Coley
192
296
0
17 Feb 2020
Differentiable Bandit Exploration
Differentiable Bandit Exploration
Craig Boutilier
Chih-Wei Hsu
Branislav Kveton
Martin Mladenov
Csaba Szepesvári
Manzil Zaheer
BDLOffRL
191
7
0
17 Feb 2020
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement
  Learning with Clairvoyant Experts
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant ExpertsIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2020
Gilwoo Lee
Brian Hou
Sanjiban Choudhury
S. Srinivasa
BDLOffRL
197
8
0
07 Feb 2020
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent
  Communication)
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)
Marek Rosa
O. Afanasjeva
Simon Andersson
Joseph Davidson
N. Guttenberg
Petr Hlubucek
Martin Poliak
Jaroslav Vítků
Jan Feyereisl
182
10
0
03 Dec 2019
Hierarchical Expert Networks for Meta-Learning
Hierarchical Expert Networks for Meta-Learning
Heinke Hihn
Daniel A. Braun
451
4
0
31 Oct 2019
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-LearningInternational Conference on Learning Representations (ICLR), 2019
L. Zintgraf
K. Shiarlis
Maximilian Igl
Sebastian Schulze
Y. Gal
Katja Hofmann
Shimon Whiteson
OffRL
320
303
0
18 Oct 2019
Gated Linear Networks
Gated Linear NetworksAAAI Conference on Artificial Intelligence (AAAI), 2019
William H. Guss
Tor Lattimore
David Budden
Avishkar Bhoopchand
Christopher Mattern
...
Ruslan Salakhutdinov
Jianan Wang
Peter Toth
Simon Schmitt
Marcus Hutter
AI4CE
217
45
0
30 Sep 2019
Recurrent Neural Processes
Recurrent Neural Processes
Timon Willi
Jonathan Masci
Jürgen Schmidhuber
Christian Osendorfer
BDL
174
18
0
13 Jun 2019
Towards Finding Longer Proofs
Towards Finding Longer ProofsInternational Conference on Theorem Proving with Analytic Tableaux and Related Methods (TABLEAUX), 2019
Zsolt Zombori
Adrián Csiszárik
Henryk Michalewski
C. Kaliszyk
Josef Urban
OffRLLRM
250
18
0
30 May 2019
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