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The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks.
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Reinforcement learning with unsupervised auxiliary tasks
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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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
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Distral: Robust multitask reinforcement learning
Yee Whye Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Critical learning periods in deep networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2018
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Discovery of useful questions as auxiliary tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, and Satinder Singh · 2019
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On warm-starting neural network training
Jordan Ash and Ryan P Adams · 2020
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Correcting momentum in temporal difference learning
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On catastrophic interference in Atari 2600 games
William Fedus, Dibya Ghosh, John D Martin, Marc G Bellemare, Yoshua Bengio, and Hugo Larochelle · 2020
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Representations for stable off-policy reinforcement learning
Dibya Ghosh and Marc G Bellemare · 2020
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DQN Zoo: Reference implementations of DQN-based agents, 2020
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Distributional reinforcement learning with quantile regression
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Deep reinforcement learning that matters
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Rainbow: Combining improvements in deep reinforcement learning
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Eigenoption discovery through the deep successor representation
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Progress & compress: A scalable framework for continual learning
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Learning to run faster in a humanoid robot soccer environment through reinforcement learning
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A geometric perspective on optimal representations for reinforcement learning
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Automatic data augmentation for generalization in reinforcement learning
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Effective evaluation of deep active learning on image classification tasks
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The value-improvement path: Towards better representations for reinforcement learning
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Spectral normalisation for deep reinforcement learning: an optimisation perspective
Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, and Razvan Pascanu · 2021
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Transient non-stationarity and generalisation in deep reinforcement learning
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Implicit under-parameterization inhibits data-efficient deep reinforcement learning
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On the effect of auxiliary tasks on representation dynamics
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Fast randomized numerical rank estimation
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