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Ensemble learning is a very prevalent method employed in machine learning.
Ensemble methods in machine learning. In International workshop on multiple classifier systems . Springer, 1–15
Thomas G Dietterich. 2000 · 2000
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Ensemble algorithms in reinforcement learning
Marco A Wiering and Hado Van Hasselt. 2008 · 2008
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Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 5026–5033
Emanuel Todorov, Tom Erez, and Yuval Tassa. 2012 · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. 2013 · 2013
Earlier work this paper cites.
Ensembles for Continuous Actions in Reinforcement Learning.. In ESANN
Siegmund Duell and Steffen Udluft. 2013 · 2013
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An ensemble of linearly combined reinforcement-learning agents. In Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence
Vukosi Ntsakisi Marivate and Michael Littman. 2013 · 2013
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Neural network ensembles in reinforcement learning
Stefan Faußer and Friedhelm Schwenker. 2015a · 2015
Earlier work this paper cites.
Selective neural network ensembles in reinforcement learning: taking the advantage of many agents
Stefan Faußer and Friedhelm Schwenker. 2015b · 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, et al · 2015
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015b · 2015
Earlier work this paper cites.
No more pesky learning rate guessing games
Leslie N Smith. 2015 · 2015
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter. 2016 · 2016
Cited alongside, same era.
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016 · 2016
Cited alongside, same era.
Deep exploration via bootstrapped DQN. In Advances in neural information processing systems . 4026–4034
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy. 2016 · 2016
Cited alongside, same era.
Sample Efficient Actor-Critic with Experience Replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, and Nando de Freitas. 2016 · 2016
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 · 2018
Later among the works it cites.
Rainbow: Combining improvements in deep reinforcement learning. In Thirty-Second AAAI Conference on Artificial Intelligence
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. 2018 · 2018
Later among the works it cites.
Model-based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, et al · 2019
Later among the works it cites.
Towards explaining the regularization effect of initial large learning rate in training neural networks. In Advances in Neural Information Processing Systems . 11674–11685
Yuanzhi Li, Colin Wei, and Tengyu Ma. 2019 · 2019
Later among the works it cites.
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Oron Anschel, Nir Baram, and Nahum Shimkin. 2017 · 2017
Cited alongside, same era.
A distributional perspective on reinforcement learning. In International Conference on Machine Learning . PMLR, 449–458
Marc G Bellemare, Will Dabney, and Rémi Munos. 2017 · 2017
Cited alongside, same era.
Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger. 2017 · 2017
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy Paul Lillicrap, Jonathan James Hunt, Alexander Pritzel, Nicolas Manfred Otto Heess, Tom Erez, Yuval Tassa, David Silver, and Daniel Pieter Wierstra. 2017 · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Yuhuai Wu, Elman Mansimov, Shun Liao, Roger B. Grosse, and Jimmy Ba. 2017 · 2017
Cited alongside, same era.
De novo structure prediction with deeplearning based scoring
R Evans, J Jumper, J Kirkpatrick, L Sifre, TFG Green, C Qin, A Zidek, A Nelson, A Bridgland, H Penedones, et al
Cited in the paper.
Trust region policy optimization. In International conference on machine learning . PMLR, 1889–1897
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. 2015a
Cited in the paper.
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2019
Later among the works it cites.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Later among the works it cites.
The break-even point on optimization trajectories of deep neural networks
Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, and Krzysztof Geras. 2020 · 2020
Closest in time.
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Kimin Lee, Michael Laskin, Aravind Srinivas, and Pieter Abbeel. 2020 · 2020
Closest in time.
Learning Rate Annealing Can Provably Help Generalization, Even for Convex Problems
Preetum Nakkiran. 2020 · 2020
Closest in time.
Curl: Contrastive unsupervised representations for reinforcement learning
Aravind Srinivas, Michael Laskin, and Pieter Abbeel. 2020 · 2020
Closest in time.