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Lifelong learning aims to create AI systems that continuously and incrementally learn during a lifetime, similar to biological learning.
A neural substrate of prediction and reward
Wolfram Schultz, Peter Dayan, and P Read Montague · 1997
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Computational models of neuromodulation
Jean-Marc Fellous and Christiane Linster · 1998
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Metalearning and neuromodulation
Kenji Doya · 2002
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Synaptic computation
LF Abbott and Wade G Regehr · 2004
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Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios
Andrea Soltoggio, John A Bullinaria, Claudio Mattiussi, Peter Dürr, and Dario Floreano · 2008
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Computational principles of synaptic memory consolidation
Marcus K Benna and Stefano Fusi · 2016
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Vizdoom: A doom-based ai research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
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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
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The forget-me-not process
Kieran Milan, Joel Veness, James Kirkpatrick, Michael Bowling, Anna Koop, and Demis Hassabis · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Neuromodulatory systems and their interactions: a review of models, theories, and experiments
Michael C Avery and Jeffrey L Krichmar · 2017
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Reinforcement learning through asynchronous advantage actor-critic on a gpu
Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, and Jan Kautz · 2017
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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
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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How many random seeds? statistical power analysis in deep reinforcement learning experiments
Cédric Colas, Olivier Sigaud, and Pierre-Yves Oudeyer · 2018
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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, et al · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Improved schemes for episodic memory-based lifelong learning
Yunhui Guo, Mingrui Liu, Tianbao Yang, and Tajana Rosing · 2020
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Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
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Unclear: A straightforward method for continual reinforcement learning
Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, and Stephen J Roberts · 2020
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Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2020
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What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
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Continual reinforcement learning with complex synapses
Christos Kaplanis, Murray Shanahan, and Claudia Clopath · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Learning battles in vizdoom via deep reinforcement learning
Kun Shao, Dongbin Zhao, Nannan Li, and Yuanheng Zhu · 2018
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Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks
Andrea Soltoggio, Kenneth O Stanley, and Sebastian Risi · 2018
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Johannes von Oswald, Christian Henning, Benjamin F. Grewe, and João Sacramento · 2020
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Supermasks in superposition
Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
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Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
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Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2021
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Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Schwarz, Siddhant Jayakumar, Razvan Pascanu, Peter E Latham, and Yee Teh · 2021
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Spacenet: Make free space for continual learning
Ghada Sokar, Decebal Constantin Mocanu, and Mykola Pechenizkiy · 2021
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Learning where to learn: Gradient sparsity in meta and continual learning
Johannes Von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, and João Sacramento · 2021
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Continual world: A robotic benchmark for continual reinforcement learning
Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, and Piotr Miłoś · 2021
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Context meta-reinforcement learning via neuromodulation
Eseoghene Ben-Iwhiwhu, Jeffery Dick, Nicholas A Ketz, Praveen K Pilly, and Andrea Soltoggio · 2022
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Same state, different task: Continual reinforcement learning without interference
Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, and Stephen J Roberts · 2022
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Signing the supermask: Keep, hide, invert
Nils Koster, Oliver Grothe, and Achim Rettinger · 2022
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Biological underpinnings for lifelong learning machines
Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. Van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, and Hava Siegelmann · 2022
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TRGP: Trust region gradient projection for continual learning
Sen Lin, Li Yang, Deliang Fan, and Junshan Zhang · 2022
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Wasserstein task embedding for measuring task similarities
Xinran Liu, Yikun Bai, Yuzhe Lu, Andrea Soltoggio, and Soheil Kolouri · 2022
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Modular lifelong reinforcement learning via neural composition
Jorge A Mendez, Harm van Seijen, and Eric Eaton · 2022
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Alexander New, Megan Baker, Eric Nguyen, and Gautam Vallabha · 2022
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Cora: Benchmarks, baselines, and metrics as a platform for continual reinforcement learning agents
Sam Powers, Eliot Xing, Eric Kolve, Roozbeh Mottaghi, and Abhinav Gupta · 2022
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A domain-agnostic approach for characterization of lifelong learning systems
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Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo de Lazcano, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, and Jordan Terry · 2023
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Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Christos Peridis, Pawel Ladosz, Jeffery Dick, Praveen K Pilly, and Soheil Kolouri · 2023
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