Fetching the paper…
Reading the bibliography…
The use of past experiences to accelerate temporal difference (TD) learning of value functions, or experience replay, is a key component in deep reinforcement learning.
A markovian decision process
Richard Bellman · 1957
Earlier work this paper cites.
Eine informationstheoretische ungleichung und ihre anwendung auf beweis der ergodizitaet von markoffschen ketten
I Csiszar · 1964
Earlier work this paper cites.
Learning to predict by the methods of temporal differences
Richard S Sutton · 1988
Earlier work this paper cites.
Self-Improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
Earlier work this paper cites.
Prioritized sweeping: Reinforcement learning with less data and less time
Andrew W Moore and Christopher G Atkeson · 1993
Earlier work this paper cites.
An analysis of Temporal-Difference learning with function approximation
John N Tsitsiklis, Member, and Benjamin Van Roy · 1997
Earlier work this paper cites.
Generalized prioritized sweeping
David Andre, Nir Friedman, and Ronald Parr · 1998
Earlier work this paper cites.
Learning and value function approximation in complex decision processes
Benjamin Van Roy · 1998
Earlier work this paper cites.
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
Earlier work this paper cites.
Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis · 2000
Earlier work this paper cites.
Eligibility traces for off-policy policy evaluation
D Precup · 2000
Earlier work this paper cites.
Off-policy temporal-difference learning with function approximation
Doina Precup, Richard S Sutton, and Sanjoy Dasgupta · 2001
Earlier work this paper cites.
Sampling techniques
William G Cochran · 2007
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
Xuanlong Nguyen, Martin J Wainwright, and Michael I Jordan · 2010
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control
E Todorov, T Erez, and Y Tassa · 2012
Earlier work this paper cites.
Planning by prioritized sweeping with small backups
Harm van Seijen and Richard S Sutton · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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.
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
Cited alongside, same era.
Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
Later among the works it cites.
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 · 2017
Later among the works it cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Later among the works it cites.
IMPALA: Scalable distributed Deep-RL with importance weighted Actor-Learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 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.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 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.
Safe and efficient Off-Policy reinforcement learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan, and Marc G Bellemare · 2016
Cited alongside, same era.
Sample efficient Actor-Critic with experience replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2016
Cited alongside, same era.
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
Later among the works it cites.
Soft Actor-Critic: Off-Policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Later among the works it cites.
Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, and David Silver · 2018
Later among the works it cites.
Remember and forget for experience replay
Guido Novati and Petros Koumoutsakos · 2018
Later among the works it cites.
Neural Temporal-Difference learning converges to global optima
Qi Cai, Zhuoran Yang, Jason D Lee, and Zhaoran Wang · 2019
Later among the works it cites.
Bias correction of learned generative models using Likelihood-Free importance weighting
Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, and Stefano Ermon · 2019
Later among the works it cites.
Off-Policy Actor-Critic with shared experience replay
Simon Schmitt, Matteo Hessel, and Karen Simonyan · 2019
Later among the works it cites.
Boosting soft Actor-Critic: Emphasizing recent experience without forgetting the past
Che Wang and Keith Ross · 2019
Later among the works it cites.
Implementation matters in deep policy gradients: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2020
Closest in time.
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché Buc, Emily Fox, and Hugo Larochelle · 2020
Closest in time.