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Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem.
Fixup initialization: Residual learning without normalization
Hongyi Zhang, Yann N. Dauphin, and Tengyu Ma · 1901
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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Playing atari with deep reinforcement learning, 2013
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Faulty reward functions in the wild, 2016
Jack Clark and Dario Amodei · 2016
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Proximal policy optimization algorithms, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures, 2018
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
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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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, et al · 2018
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Vizdoom competitions: Playing doom from pixels
Marek Wydmuch, Michał Kempka, and Wojciech Jaśkowski · 2018
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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
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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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
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Obstacle tower: A generalization challenge in vision, control, and planning
Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius, and Danny Lange · 2019
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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
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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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
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Autonomous navigation of stratospheric balloons using reinforcement learning
Keep CALM and explore: Language models for action generation in text-based games
Shunyu Yao, Rohan Rao, Matthew Hausknecht, and Karthik Narasimhan · 2020
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Towards robust and domain agnostic reinforcement learning competitions: Minerl 2020
William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, et al · 2021
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Benchmarking the spectrum of agent capabilities
Danijar Hafner · 2021
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How to train your robot with deep reinforcement learning: lessons we have learned
Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, and Sergey Levine · 2021
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Distilling reinforcement learning tricks for video games
Anssi Kanervisto, Christian Scheller, Yanick Schraner, and Ville Hautamäki · 2021
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Marc G Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C Machado, Subhodeep Moitra, Sameera S Ponda, and Ziyu Wang · 2020
Cited alongside, same era.
Pytorchrl: Modular and distributed reinforcement learning in pytorch
Albert Bou and Gianni De Fabritiis · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman · 2020
Cited alongside, same era.
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
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The nethack learning environment
Heinrich Küttler, Nantas Nardelli, Alexander H Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, and Tim Rocktäschel · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Flatland-rl: Multi-agent reinforcement learning on trains
Sharada Mohanty, Erik Nygren, Florian Laurent, Manuel Schneider, Christian Scheller, Nilabha Bhattacharya, Jeremy Watson, Adrian Egli, Christian Eichenberger, Christian Baumberger, et al · 2020
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Julia Kiseleva, Ziming Li, Mohammad Aliannejadi, Shrestha Mohanty, Maartje ter Hoeve, Mikhail Burtsev, Alexey Skrynnik, Artem Zholus, Aleksandr Panov, Kavya Srinet, et al · 2021
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Hpc resources of the higher school of economics
PS Kostenetskiy, RA Chulkevich, and VI Kozyrev · 2021
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Juewu-mc: Playing minecraft with sample-efficient hierarchical reinforcement learning
Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, and Wei Yang · 2021
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Pretrained transformers as universal computation engines
Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch · 2021
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Seihai: A sample-efficient hierarchical ai for the minerl competition
Hangyu Mao, Chao Wang, Xiaotian Hao, Yihuan Mao, Yiming Lu, Chengjie Wu, Jianye Hao, Dong Li, and Pingzhong Tang · 2021
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Language models are few-shot butlers
Vincent Micheli and Francois Fleuret · 2021
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Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Gražvydas Šemetulskis, João Schapke, Jonas Kubilius, Jurgis Pašukonis, et al · 2021
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The minerl basalt competition on learning from human feedback
Rohin Shah, Cody Wild, Steven H Wang, Neel Alex, Brandon Houghton, William Guss, Sharada Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, et al · 2021
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Can wikipedia help offline reinforcement learning?
Machel Reid, Yutaro Yamada, and Shixiang Shane Gu · 2022
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