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In reinforcement learning, Return, which is the weighted accumulated future rewards, and Value, which is the expected return, serve as the objective that guides the learning of the policy.
On learning intrinsic rewards for policy gradient methods
Zeyu Zheng, Junhyuk Oh, and Satinder Singh · 1905
Earlier work this paper cites.
Scaling reinforcement learning algorithms by learning variable temporal resolution models
Satinder P Singh · 1992
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Temporal-difference methods and markov models
Etienne Barnard · 1993
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Introduction to reinforcement learning , volume 135
Richard S Sutton, Andrew G Barto, et al · 1998
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew G Barto, and Satinder P Singh · 2005
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What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
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Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
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Time consistent discounting
Tor Lattimore and Marcus Hutter · 2011
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Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Richard S Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M Pilarski, Adam White, and Doina Precup · 2011
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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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
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
How to discount deep reinforcement learning: Towards new dynamic strategies
Vincent François-Lavet, Raphael Fonteneau, and Damien Ernst · 2015
Cited alongside, same era.
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 · 2015
Cited alongside, same era.
Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Rudder: Return decomposition for delayed rewards
Jose A Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, and Sepp Hochreiter · 2018
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Using reward machines for high-level task specification and decomposition in reinforcement learning
Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano, and Sheila McIlraith · 2018
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Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Mattchantk/gym-maze, 2016
MattChanTK · 2016
Cited alongside, same era.
Learning to navigate in complex environments
Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C Stadie, Sergey Levine, and Pieter Abbeel · 2016
Cited alongside, same era.
Reinforcement learning thorugh asynchronous advantage actor-critic on a gpu
Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, and Jan Kautz · 2017
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
Cited alongside, same era.
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, and Michael Bowling · 2017
Cited alongside, same era.
Later among the works it cites.
Openai five
OpenAI · 2018
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Generalizing value estimation over timescale
Craig Sherstan, James MacGlashan, and Patrick M Pilarski · 2018
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Meta-gradient reinforcement learning
Zhongwen Xu, Hado P van Hasselt, and David Silver · 2018
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Rethinking the discount factor in reinforcement learning: A decision theoretic approach
Silviu Pitis · 2019
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Separating value functions across time-scales
Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Emma Brunskill, and Joelle Pineau · 2019
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Discovery of useful questions as auxiliary tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L Lewis, Junhyuk Oh, Hado P van Hasselt, David Silver, and Satinder Singh · 2019
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Discovering reinforcement learning algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, and David Silver · 2020
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Meta-gradient reinforcement learning with an objective discovered online
Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, and David Silver · 2020
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Self-tuning deep reinforcement learning
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, and Satinder Singh · 2020
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