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A key challenge in training generally-capable agents is the design of training tasks that facilitate broad generalization and robustness to environment variations.
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Garud N Iyengar · 2005
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Robust reinforcement learning
Jun Morimoto and Kenji Doya · 2005
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Robust control of markov decision processes with uncertain transition matrices
Arnab Nilim and Laurent El Ghaoui · 2005
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An experiment in automatic game design
Julian Togelius and Jurgen Schmidhuber · 2008
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Evolutionary game design
Cameron Browne and Frederic Maire · 2010
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Robust online optimization of reward-uncertain mdps
Kevin Regan and Craig Boutilier · 2011
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Search-based procedural content generation: A taxonomy and survey
Julian Togelius, Georgios N Yannakakis, Kenneth O Stanley, and Cameron Browne · 2011
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Regret-based reward elicitation for markov decision processes
Kevin Regan and Craig Boutilier · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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OpenAI Gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Safe policy improvement by minimizing robust baseline regret
Mohammad Ghavamzadeh, Marek Petrik, and Yinlam Chow · 2016
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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, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Delving into adversarial attacks on deep policies
Jernej Kos and Dawn Song · 2017
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Tactics of adversarial attack on deep reinforcement learning agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun · 2017
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Supervision via competition: Robot adversaries for learning tasks
Lerrel Pinto, James Davidson, and Abhinav Gupta · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, and Rob Fergus · 2017
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Minimalistic gridworld environment for OpenAI Gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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A dissection of overfitting and generalization in continuous reinforcement learning
Off-belief learning
Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, Noam Brown, and Jakob Foerster · 2021
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A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2021
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Open-ended learning leads to generally capable agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michaël Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, and Wojciech Marian Czarnecki · 2021
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Asymmetric self-play for automatic goal discovery in robotic manipulation, 2021
OpenAI OpenAI, Matthias Plappert, Raul Sampedro, Tao Xu, Ilge Akkaya, Vineet Kosaraju, Peter Welinder, Ruben D’Sa, Arthur Petron, Henrique Ponde de Oliveira Pinto, Alex Paino, Hyeonwoo Noh, Lilian Weng, Qiming Yuan, Casey Chu, and Wojciech Zaremba · 2021
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Amy Zhang, Nicolas Ballas, and Joelle Pineau · 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, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang · 2019
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Adversarial policies: Attacking deep reinforcement learning
Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, and Stuart Russell · 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, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
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Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O. Stanley · 2019
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Emergent complexity and zero-shot transfer via unsupervised environment design
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine · 2020
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Co-generation of game levels and game-playing agents
Aaron Dharna, Julian Togelius, and L. B. Soros · 2020
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Observational overfitting in reinforcement learning
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, and Behnam Neyshabur · 2020
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Sam Witty, Jun K Lee, Emma Tosch, Akanksha Atrey, Kaleigh Clary, Michael L Littman, and David Jensen · 2021
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Robust domain randomised reinforcement learning through peer-to-peer distillation
Chenyang Zhao and Timothy Hospedales · 2021
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Deep surrogate assisted generation of environments
Varun Bhatt, Bryon Tjanaka, Matthew C Fontaine, and Stefanos Nikolaidis · 2022
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Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, et al · 2022
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Transfer dynamics in emergent evolutionary curricula
Aaron Dharna, Amy K Hoover, Julian Togelius, and Lisa Soros · 2022
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Goal misgeneralization in deep reinforcement learning
Lauro Langosco Di Langosco, Jack Koch, Lee D Sharkey, Jacob Pfau, and David Krueger · 2022
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It takes four to tango: Multiagent selfplay for automatic curriculum generation
Yuqing Du, Pieter Abbeel, and Aditya Grover · 2022
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Grounding aleatoric uncertainty in unsupervised environment design
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, and Jakob Foerster · 2022
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Mutation models: Learning to generate levels by imitating evolution
Ahmed Khalifa, Julian Togelius, and Michael Cerny Green · 2022
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Evolving curricula with regret-based environment design
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, and Tim Rocktäschel · 2022
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In the zone: Measuring difficulty and progression in curriculum generation
Rose E Wang, Jesse Mu, Dilip Arumugam, Natasha Jaques, and Noah Goodman · 2022
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Human-timescale adaptation in an open-ended task space, 2023
Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, and Lei Zhang · 2023
Closest in time.
Human-timescale adaptation in an open-ended task space
Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, et al · 2023
Closest in time.