Fetching the paper…
Reading the bibliography…
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc V. Le, and Ruslan Salakhutdinov · 1901
Earlier work this paper cites.
TF-Replicator: Distributed Machine Learning for Researchers
Peter Buchlovsky, David Budden, Dominik Grewe, Chris Jones, John Aslanides, Frederic Besse, Andy Brock, Aidan Clark, Sergio Gomez Colmenarejo, Aedan Pope, Fabio Viola, and Dan Belov · 1902
Earlier work this paper cites.
Simple statistical gradient-following methods for connectionist reinforcement learning
Ronald J. Williams · 1992
Earlier work this paper cites.
Convergence Properties of Evolution Strategies with the Derandomized Covariance Matrix Adaptation: CMA-ES
Nikolaus Hansen, Andreas Ostermeier, and Andreas Ostermeier · 1997
Earlier work this paper cites.
A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants
Radford M. Neal and Geoffrey E. Hinton · 1998
Earlier work this paper cites.
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 1998
Earlier work this paper cites.
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.
Relative Entropy Policy Search
Jan Peters, M Katharina, and Yasemin Altün · 2008
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 · 2012
Earlier work this paper cites.
Learning continuous control policies by stochastic value gradients
Nicolas Heess, Greg Wayne, David Silver, Timothy P. Lillicrap, Yuval Tassa, and Tom Erez · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
Earlier work this paper cites.
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
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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Trust Region Policy Optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 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.
Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
Cited alongside, same era.
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Sergey Levine · 2018
Later among the works it cites.
Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess · 2018
Later among the works it cites.
Kickstarting Deep Reinforcement Learning
Simon Schmitt, Jonathan J. Hudson, Augustin Zídek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Küttler, Andrew Zisserman, Karen Simonyan, and S. M. Ali Eslami · 2018
Later among the works it cites.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 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…
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Tim Harley, Timothy P Lillicrap, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Cited alongside, same era.
Learning functions across many orders of magnitudes
Hado van Hasselt, Arthur Guez, Matteo Hessel, and David Silver · 2016
Cited alongside, same era.
Deriving and Improving CMA-ES with Information Geometric Trust Regions
Abbas Abdolmaleki, Bob Price, Nuno Lau, Luis P Reis, and Gerhard Neumann · 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.
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
Cited alongside, same era.
Cloud TPU, 2018
Google · 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.
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy P. Lillicrap, and Martin A. Riedmiller · 2018
Later among the works it cites.
Variance reduction for policy gradient with action-dependent factorized baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham Kakade, Igor Mordatch, and Pieter Abbeel · 2018
Later among the works it cites.
Off-Policy Actor-Critic with Shared Experience Replay
Anonymous Authors · 2019
Closest in time.
AlphaStar: Mastering the Real-Time Strategy Game StarCraft II, 2019
DeepMind · 2019
Closest in time.
Human-level performance in 3d multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castañeda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, and Thore Graepel · 2019
Closest in time.
Recurrent Experience Replay in Distributed Reinforcement Learning
Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, and Will Dabney · 2019
Closest in time.
Hierarchical Visuomotor Control of Humanoids
Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, and Greg Wayne · 2019
Closest in time.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Closest in time.
Supervised Policy Update for Deep Reinforcement Learning
Quan Vuong, Keith Ross, and Yiming Zhang · 2019
Closest in time.