Fetching the paper…
Reading the bibliography…
Artificial neural networks are promising for general function approximation but challenging to train on non-independent or non-identically distributed data due to catastrophic forgetting.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Learning from Delayed Rewards
Chris Watkins · 1989
Earlier work this paper cites.
Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
Earlier work this paper cites.
On the rank of random matrices
Colin Cooper · 2000
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Playing Atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, and Ioannis Antonoglou · 2013
Earlier work this paper cites.
Online multi-task learning for policy gradient methods
Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, and Matthew Taylor · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
Earlier work this paper cites.
Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, and Yuval Tassa · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Earlier work this paper cites.
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.
PyGame learning environment
Norman Tasfi · 2016
Earlier work this paper cites.
Deep reinforcement learning with double Q-learning
Hado van Hasselt, Arthur Guez, and David Silver · 2016
Earlier work this paper cites.
Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 2017
Earlier work this paper cites.
PathNet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
Earlier work this paper cites.
Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Earlier work this paper cites.
iCaRL: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Earlier work this paper cites.
Adapting kernel representations online using submodular maximization
Matthew Schlegel, Yangchen Pan, Jiecao Chen, and Martha White · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Spinning up in deep reinforcement learning
Joshua Achiam · 2018
Cited alongside, same era.
Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
MinAtar: An Atari-inspired testbed for more efficient reinforcement learning experiments
Kenny Young and Tian Tian · 2019
Later among the works it cites.
Continual learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2019
Later among the works it cites.
Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
Later among the works it cites.
Improving performance in reinforcement learning by breaking generalization in neural networks
Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, and Adam White · 2020
Later among the works it cites.
Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Cited alongside, same era.
Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 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.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
Cited alongside, same era.
Selective experience replay for lifelong learning
David Isele and Akansel Cosgun · 2018
Cited alongside, same era.
Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2018
Cited alongside, same era.
PackNet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
Cited alongside, same era.
Xisen Jin, Arka Sadhu, Junyi Du, and Xiang Ren · 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.
Maxmin Q-learning: Controlling the estimation bias of q-learning
Qingfeng Lan, Yangchen Pan, Alona Fyshe, and Martha White · 2020
Later among the works it cites.
Lifelong policy gradient learning of factored policies for faster training without forgetting
Jorge Mendez, Boyu Wang, and Eric Eaton · 2020
Later among the works it cites.
Frequency-based search-control in dyna
Yangchen Pan, Jincheng Mei, and Amir massoud Farahmand · 2020
Later among the works it cites.
Lifelong generative modeling
Jason Ramapuram, Magda Gregorova, and Alexandros Kalousis · 2020
Later among the works it cites.
Mastering Atari, Go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
Later among the works it cites.
Functional regularisation for continual learning with gaussian processes
Michalis K Titsias, Jonathan Schwarz, Alexander G de G Matthews, Razvan Pascanu, and Yee Whye Teh · 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
Later among the works it cites.
Improving computational efficiency in visual reinforcement learning via stored embeddings
Lili Chen, Kimin Lee, Aravind Srinivas, and Pieter Abbeel · 2021
Later among the works it cites.
Dual-teacher class-incremental learning with data-free generative replay
Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee · 2021
Later among the works it cites.
A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars · 2021
Later among the works it cites.
Ternary feature masks: zero-forgetting for task-incremental learning
Marc Masana, Tinne Tuytelaars, and Joost Van de Weijer · 2021
Later among the works it cites.
SpaceNet: Make free space for continual learning
Ghada Sokar, Decebal Constantin Mocanu, and Mykola Pechenizkiy · 2021
Later among the works it cites.
Atari-5: Distilling the arcade learning environment down to five games
Matthew Aitchison, Penny Sweetser, and Marcus Hutter · 2022
Closest in time.
Online continual learning for embedded devices
Tyler L Hayes and Christopher Kanan · 2022
Closest in time.
Modular lifelong reinforcement learning via neural composition
Jorge A Mendez, Harm van Seijen, and Eric Eaton · 2022
Closest in time.
A walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning
Laura Smith, Ilya Kostrikov, and Sergey Levine · 2022
Closest in time.
Tianshou: A highly modularized deep reinforcement learning library
Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, and Jun Zhu · 2022
Closest in time.
Real-time reinforcement learning for vision-based robotics utilizing local and remote computers
Yan Wang, Gautham Vasan, and A Rupam Mahmood · 2023
Closest in time.