Fetching the paper…
Reading the bibliography…
We develop a new continual meta-learning method to address challenges in sequential multi-task learning.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Learning a synaptic learning rule
Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier · 1990
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
Earlier work this paper cites.
Jürgen Schmidhuber · 2011
Earlier work this paper cites.
First experiments with powerplay
Rupesh Kumar Srivastava, Bas R. Steunebrink, and Jürgen Schmidhuber · 2012
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Variable-shot adaptation for online meta-learning
Tianhe Yu, Xinyang Geng, Chelsea Finn, and Sergey Levine · 2012
Earlier work this paper cites.
Net2net: Accelerating learning via knowledge transfer
Tianqi Chen, Ian Goodfellow, and Jonathon Shlens · 2015
Earlier work this paper cites.
Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
Earlier work this paper cites.
Lifelong machine learning
Zhiyuan Chen and Bing Liu · 2016
Earlier work this paper cites.
Introduction to online convex optimization
Elad Hazan et al · 2016
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Earlier work this paper cites.
Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Lei Jimmy Ba, and Ruslan Salakhutdinov · 2016
Earlier work this paper cites.
Progressive Neural Networks
Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Earlier work this paper cites.
A Deep Hierarchical Approach to Lifelong Learning in Minecraft
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz, and Shie Mannor · 2016
Earlier work this paper cites.
Learning to reinforcement learn
Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Rémi Munos, Charles Blundell, Dharshan Kumaran, and Matthew Botvinick · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
Earlier work this paper cites.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
Earlier work this paper cites.
Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Learning to learn: Meta-critic networks for sample efficient learning
Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, and Yongxin Yang · 2017
Earlier work this paper cites.
Distral: Robust multitask reinforcement learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
Earlier work this paper cites.
Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2018
Cited alongside, same era.
Progressive reinforcement learning with distillation for multi-skilled motion control
Glen Berseth, Cheng Xie, Paul Cernek, and Michiel Van de Panne · 2018
Cited alongside, same era.
Learning to adapt: Meta-learning for model-based control
Ignasi Clavera, Anusha Nagabandi, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
Cited alongside, same era.
IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
Cited alongside, same era.
Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm
Chelsea Finn and Sergey Levine · 2018
Meta-learning representations for continual learning
Khurram Javed and Martha White · 2019
Later among the works it cites.
Reconciling meta-learning and continual learning with online mixtures of tasks
Ghassen Jerfel, Erin Grant, Tom Griffiths, and Katherine A Heller · 2019
Later among the works it cites.
Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar · 2019
Later among the works it cites.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
Later among the works it cites.
Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
Later among the works it cites.
Deep online learning via meta-learning: Continual adaptation for model-based RL
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Divide-and-conquer reinforcement learning
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, and Sergey Levine · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Evolved policy gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel · 2018
Cited alongside, same era.
Selective experience replay for lifelong learning
David Isele and Akansel Cosgun · 2018
Cited alongside, same era.
Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, and Richard S Zemel · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Cited alongside, same era.
Promp: Proximal meta-policy search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, and Pieter Abbeel · 2018
Cited alongside, same era.
Anusha Nagabandi, Chelsea Finn, and Sergey Levine · 2019
Later among the works it cites.
Continual lifelong learning with neural networks: A review
German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2019
Later among the works it cites.
Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, and Sergey Levine · 2019
Later among the works it cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Later among the works it cites.
Dream: A challenge data set and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie · 2019
Later among the works it cites.
Online meta-learning on non-convex setting
Zhenxun Zhuang, Yunlong Wang, Kezi Yu, and Songtao Lu · 2019
Later among the works it cites.
Fast context adaptation via meta-learning
Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
Later among the works it cites.
Defining benchmarks for continual few-shot learning
Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, and Amos Storkey · 2020
Later among the works it cites.
Offline meta reinforcement learning
Ron Dorfman and Aviv Tamar · 2020
Later among the works it cites.
Look-ahead meta learning for continual learning
Gunshi Gupta, Karmesh Yadav, and Liam Paull · 2020
Later among the works it cites.
Meta-consolidation for continual learning
Joseph K J and Vineeth N Balasubramanian · 2020
Later among the works it cites.
Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2020
Later among the works it cites.
Offline meta-reinforcement learning with advantage weighting
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, and Chelsea Finn · 2020
Later among the works it cites.
Are we overfitting to experimental setups in recognition?, 2020
Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, and Ali Farhadi · 2020
Later among the works it cites.
Online structured meta-learning
Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher, and Caiming Xiong · 2020
Later among the works it cites.
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2020
Later among the works it cites.
Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting
Craig Atkinson, Brendan McCane, Lech Szymanski, and Anthony Robins · 2021
Closest in time.