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Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods.
Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 1912
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A Guide to the Mazes of Menace , 1987
Eric S. Raymond · 1987
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Feudal reinforcement learning
Peter Dayan and Geoffrey E. Hinton · 1992
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Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Reinforcement learning with hierarchies of machines
Ronald Parr and Stuart J. Russell · 1997
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
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A survey of robot learning from demonstration
Brenna Argall, Sonia Chernova, Manuela M. Veloso, and Brett Browning · 2009
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Transfer learning for reinforcement learning domains: A survey
Matthew E. Taylor and Peter Stone · 2009
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Learning to win by reading manuals in a monte-carlo framework
S. R. K. Branavan, David Silver, and Regina Barzilay · 2011
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The arcade learning environment: An evaluation platform for general agents
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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A survey of real-time strategy game ai research and competition in starcraft
Santiago Ontanón, Gabriel Synnaeve, Alberto Uriarte, Florian Richoux, David Churchill, and Mike Preuss · 2013
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, and Stig Petersen · 2016
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
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Vizdoom: A doom-based ai research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
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TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, and Nicolas Usunier · 2016
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Deep reinforcement learning: A brief survey
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath · 2017
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Learning combat in NetHack
Jonathan Campbell and Clark Verbrugge · 2017
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Stabilising experience replay for deep multi-agent reinforcement learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H.S. Torr, Pushmeet Kohli, and Shimon Whiteson · 2017
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Deep q-learning from demonstrations
Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Joel Z. Leibo, and Audrunas Gruslys · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron van den Oord, and Rémi Munos · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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# Exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
Cited alongside, same era.
Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Manfred Otto Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
TorchBeast: A PyTorch Platform for Distributed RL
Heinrich Küttler, Nantas Nardelli, Thibaut Lavril, Marco Selvatici, Viswanath Sivakumar, Tim Rocktäschel, and Edward Grefenstette · 2019
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A survey of reinforcement learning informed by natural language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, and Tim Rocktäschel · 2019
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Value propagation networks
Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H.S. Torr, and Nicolas Usunier · 2019
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Continual lifelong learning with neural networks: A review
German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2019
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Sebastian Risi and Julian Togelius · 2019
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Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, et al · 2017
Cited alongside, same era.
Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, and Nando de Freitas · 2018
Cited alongside, same era.
Exploration in NetHack with secret discovery
Jonathan Campbell and Clark Verbrugge · 2018
Cited alongside, same era.
IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
Cited alongside, same era.
Illuminating generalization in deep reinforcement learning through procedural level generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, and Sebastian Risi · 2018
Cited alongside, same era.
Unicorn: Continual learning with a universal, off-policy agent
Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, and Tom Schaul · 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.
Later among the works it cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G.J. Rudner, Chia-Man Hung, Philip H.S. Torr, Jakob Foerster, and Shimon Whiteson · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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SWAGGINZZZ
Aransentin Breggan Hampe Pellsson · 2020
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Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2020
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List of Nethack spoilers
Eva Myers · 2020
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Reinforcement Learning for roguelike type games (eliteMod v0.9)
Juan Manuel Sanchez Fernandez · 2020
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Emergent systematic generalization in a situated agent
Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, and Adam Santoro · 2020
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Berlin Interpretation
International Roguelike Development Conference · 2020
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NetHack Home Page
Kenneth Lorber · 2020
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Public NetHack server at alt.org (NAO)
M. Drew Streib · 2020
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NetHackWiki
NetHack Wiki · 2020
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RIDE: Rewarding impact-driven exploration for procedurally-generated environments
Roberta Raileanu and Tim Rocktäschel · 2020
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A Guide to the Mazes of Menace: Guidebook for NetHack
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TAEB Documentation: Other Bots
TAEB · 2020
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Rotation, translation, and cropping for zero-shot generalization
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RTFM: Generalising to new environment dynamics via reading
Victor Zhong, Tim Rocktäschel, and Edward Grefenstette · 2020
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