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In value-based deep reinforcement learning with replay memories, the batch size parameter specifies how many transitions to sample for each gradient update.
Self-improving reactive agents based on reinforcement learning, planning and teaching
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Matplotlib: A 2d graphics environment
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The arcade learning environment: An evaluation platform for general agents
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Adam: A method for stochastic optimization
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Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
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Reproducibility of benchmarked deep reinforcement learning tasks for continuous control, 2017
Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup · 2017
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2017
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Jax: composable transformations of python+ numpy programs
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, et al · 2018
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Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G. Bellemare · 2018
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IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
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Noisy networks for exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alexander Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, and Shane Legg · 2018
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On the computational inefficiency of large batch sizes for stochastic gradient descent
Noah Golmant, Nikita Vemuri, Zhewei Yao, Vladimir Feinberg, Amir Gholami, Kai Rothauge, Michael W Mahoney, and Joseph Gonzalez · 2018
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Rainbow: Combining Improvements in Deep Reinforcement learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2018
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Revisiting small batch training for deep neural networks
Dominic Masters and Carlo Luschi · 2018
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Parameter space noise for exploration
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Improving computational efficiency in visual reinforcement learning via stored embeddings
Lili Chen, Kimin Lee, Aravind Srinivas, and Pieter Abbeel · 2021
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The value-improvement path: Towards better representations for reinforcement learning
Will Dabney, Andre Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, and David Silver · 2021
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Spectral normalisation for deep reinforcement learning: An optimisation perspective
Florin Gogianu, Tudor Berariu, Mihaela C Rosca, Claudia Clopath, Lucian Busoniu, and Razvan Pascanu · 2021
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Large batch experience replay
Thibault Lahire, Matthieu Geist, and Emmanuel Rachelson · 2021
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Remember more by recalling less: Investigating the role of batch size in continual learning with experience replay (student abstract)
Maciej Wołczyk and Andrii Krutsylo · 2021
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Accelerated methods for deep reinforcement learning
Adam Stooke and Pieter Abbeel · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Measuring the effects of data parallelism on neural network training
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When to use parametric models in reinforcement learning?
Hado P van Hasselt, Matteo Hessel, and John Aslanides · 2019
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Revisiting fundamentals of experience replay
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Array programming with numpy
Charles R Harris, K Jarrod Millman, Stéfan J Van Der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al · 2020
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Model based reinforcement learning for atari
Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski · 2020
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Beyond tabula rasa: Reincarnating reinforcement learning
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, and Marc G Bellemare · 2022
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Batch size-invariance for policy optimization
Jacob Hilton, Karl Cobbe, and John Schulman · 2022
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The primacy bias in deep reinforcement learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, and Aaron Courville · 2022
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Penalizing gradient norm for efficiently improving generalization in deep learning
Yang Zhao, Hao Zhang, and Xiuyuan Hu · 2022
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Compute-efficient deep learning: Algorithmic trends and opportunities
Brian R Bartoldson, Bhavya Kailkhura, and Davis Blalock · 2023
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Sample-efficient reinforcement learning by breaking the replay ratio barrier
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Pink noise is all you need: Colored noise exploration in deep reinforcement learning
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Proto-value networks: Scaling representation learning with auxiliary tasks
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Exploration in deep reinforcement learning: From single-agent to multiagent domain
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Understanding plasticity in neural networks
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Bigger, better, faster: Human-level atari with human-level efficiency
Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal, and Pablo Samuel Castro · 2023
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The dormant neuron phenomenon in deep reinforcement learning
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, and Utku Evci · 2023
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