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Training deep reinforcement learning (DRL) models usually requires high computation costs.
Understanding multi-step deep reinforcement learning: A systematic study of the dqn target
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Song Han, Huizi Mao, and William J Dally · 2016
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Policy distillation
Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2016
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Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein · 2017
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Xin Dong, Shangyu Chen, and Sinno Pan · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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Pruning convolutional neural networks for resource efficient inference
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Accelerating the deep reinforcement learning with neural network compression
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Successfully applying the stabilized lottery ticket hypothesis to the transformer architecture
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Pops: Policy pruning and shrinking for deep reinforcement learning
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Learning sparse neural networks through l _ 0 l\_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
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Kickstarting deep reinforcement learning
Simon Schmitt, Jonathan J Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech M Czarnecki, Joel Z Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, et al · 2018
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Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, and Gianluca Francini · 2018
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To prune, or not to prune: exploring the efficacy of pruning for model compression
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Evaluating lottery tickets under distributional shifts
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Playing the lottery with rewards and multiple languages: lottery tickets in rl and nlp
Haonan Yu, Sergey Edunov, Yuandong Tian, and Ari S Morcos · 2020
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Single-shot pruning for offline reinforcement learning
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A unified lottery ticket hypothesis for graph neural networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang · 2021
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Transient non-stationarity and generalisation in deep reinforcement learning
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Gst: Group-sparse training for accelerating deep reinforcement learning
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Jonathan Schwarz, Siddhant Jayakumar, Razvan Pascanu, Peter E Latham, and Yee Teh · 2021
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Dynamic sparse training for deep reinforcement learning
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Magnetic control of tokamak plasmas through deep reinforcement learning
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