Fetching the paper…
Reading the bibliography…
Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development.
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.
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.
Prioritized experience replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
Earlier work this paper cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Earlier work this paper cites.
The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Control of memory, active perception, and action in Minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, and Honglak Lee · 2016
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.
Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Earlier work this paper cites.
Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
Earlier work this paper cites.
Pre-training neural networks with human demonstrations for deep reinforcement learning
Gabriel V Cruz Jr, Yunshu Du, and Matthew E Taylor · 2017
Earlier work this paper cites.
One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Hierarchical and interpretable skill acquisition in multi-task reinforcement learning
Tianmin Shu, Caiming Xiong, and Richard Socher · 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.
A deep hierarchical approach to lifelong learning in minecraft
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz, and Shie Mannor · 2017
Cited alongside, same era.
https://blog.openai.com/ai-and-compute/, May 2018
Dario Amodei and Danny Hernandez · 2018
Cited alongside, same era.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Later among the works it cites.
ChainerRL: A deep reinforcement learning library
Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, and Takahiro Ishikawa · 2019
Later among the works it cites.
The MineRL competition on sample efficient reinforcement learning using human priors
William H. Guss, Cayden Codel*, Katja Hofmann*, Brandon Houghton*, Noboru Kuno*, Stephanie Milani*, Sharada Mohanty*, Diego Perez Liebana*, Ruslan Salakhutdinov*, Nicholay Topin*, Manuela Veloso*, and Phillip Wang* · 2019
Later among the works it cites.
MineRL: A large-scale dataset of Minecraft demonstrations
William H. Guss*, Brandon Houghton*, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, and Ruslan Salakhutdinov · 2019
Later among the works it cites.
Ai takes on popular minecraft game in machine-learning contest, Nov 2019
Jeremy Hsu · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alphastar: Mastering the real-time strategy game starcraft ii, 2018
DeepMind · 2018
Cited alongside, same era.
Reinforcement learning from imperfect demonstrations
Yang Gao, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell, et al · 2018
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
Deep q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al · 2018
Cited alongside, same era.
Learning to run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
Łukasz Kidziński, Sharada P Mohanty, Carmichael F Ong, Jennifer L Hicks, Sean F Carroll, Sergey Levine, Marcel Salathé, and Scott L Delp · 2018
Cited alongside, same era.
Gotta learn fast: A new benchmark for generalization in rl
Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, and John Schulman · 2018
Cited alongside, same era.
Openai five, Sep 2018
OpenAI · 2018
Cited alongside, same era.
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu Kuno, Andre Kramer, Sam Devlin, Raluca D Gaina, and Daniel Ionita · 2019
Later among the works it cites.
Sqil: Imitation learning via reinforcement learning with sparse rewards
Siddharth Reddy, Anca D Dragan, and Sergey Levine · 2019
Later among the works it cites.
Minecraft diamond challenge leaves ai creators stumped, Dec 2019
Sam Shead · 2019
Later among the works it cites.
Neurips 2019 will host minecraft reinforcement learning competition, May 2019
Synced · 2019
Later among the works it cites.
Ai has bested chess and go, but it struggles to find a diamond in minecraft, Dec 2019
James Vincent · 2019
Later among the works it cites.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michael Mathieu, Andrew Dudzik, Junyong Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Later among the works it cites.
Guaranteeing reproducibility in deep learning competitions
Brandon Houghton, Stephanie Milani, Nicholay Topin, William Guss, Katja Hofmann, Diego Perez-Liebana, Manuela Veloso, and Ruslan Salakhutdinov · 2020
Later among the works it cites.
Retrospective analysis of the 2019 MineRL competition on sample efficient reinforcement learning
Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Keisuke Nakata, Oriol Vinyals, and Noboru Sean Kuno · 2020
Later among the works it cites.