Fetching the paper…
Reading the bibliography…
Value estimation is a critical component of the reinforcement learning (RL) paradigm.
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
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
Model regularization for stable sample rollouts
Erik Talvitie · 2014
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
Earlier work this paper cites.
Learning using privileged information: similarity control and knowledge transfer
Vladimir Vapnik and Rauf Izmailov · 2015
Earlier work this paper cites.
Deep reinforcement learning with double Q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Earlier work this paper cites.
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Earlier work this paper cites.
Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2017
Earlier work this paper cites.
Value prediction network
Junhyuk Oh, Satinder Singh, and Honglak Lee · 2017
Earlier work this paper cites.
Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Imagination-augmented agents for deep reinforcement learning
Sébastien Racanière, Théophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomenech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
The predictron: End-to-end learning and planning
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, et al · 2017
Cited alongside, same era.
Woulda, coulda, shoulda: Counterfactually-guided policy search
Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, and Nicolas Heess · 2018
Reinforcement and imitation learning for diverse visuomotor skills
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei A. Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, and Nicolas Heess · 2018
Later among the works it cites.
DeepMDP: Learning continuous latent space models for representation learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, and Marc G. Bellemare · 2019
Later among the works it cites.
An investigation of model-free planning
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, et al · 2019
Later among the works it cites.
Hindsight credit assignment
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, and Remi Munos · 2019
Later among the works it cites.
Recurrent experience replay in distributed reinforcement learning
Steven Kapturowski, Georg Ostrovski, Will Dabney, John Quan, and Remi Munos · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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, et al · 2018
Cited alongside, same era.
Iterative value-aware model learning
Amir-massoud Farahmand · 2018
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
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
Mastering Atari, Go, Chess and Shogi by planning with a learned model
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.
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
Later among the works it cites.
Credit assignment techniques in stochastic computation graphs
Théophane Weber, Nicolas Heess, Lars Buesing, and David Silver · 2019
Later among the works it cites.