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Robust reinforcement learning is the problem of learning control policies that provide optimal worst-case performance against a span of adversarial environments.
Behaviour suite for reinforcement learning
Osband, Ian, Doron, Yotam, Hessel, Matteo, Aslanides, John, Sezener, Eren, Saraiva, Andre, McKinney, Katrina, Lattimore, Tor, Szepesvari, Csaba, Singh, Satinder, et al · 1908
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Solving Rubik’s Cube with a Robot Hand
OpenAI, Akkaya, Ilge, Andrychowicz, Marcin, Chociej, Maciek, Litwin, Mateusz, McGrew, Bob, Petron, Arthur, Paino, Alex, Plappert, Matthias, Powell, Glenn, Ribas, Raphael, Schneider, Jonas, Tezak, Nikolas, Tworek, Jerry, Welinder, Peter, Weng, Lilian, Yuan, Qiming, Zaremba, Wojciech, & Zhang, Lei. 2019 · 1910
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Leveraging Procedural Generation to Benchmark Reinforcement Learning
Cobbe, Karl, Hesse, Christopher, Hilton, Jacob, & Schulman, John. 2019 · 1912
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Markov games as a framework for multi-agent reinforcement learning
Littman, Michael L. 1994 · 1994
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Robust dynamic programming
Iyengar, Garud. 2022 · 2002
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Robust dynamic programming
Iyengar, Garud N. 2005 · 2005
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Robust reinforcement learning
Morimoto, Jun, & Doya, Kenji. 2005 · 2005
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Robust control of Markov decision processes with uncertain transition matrices
Nilim, Arnab, & El Ghaoui, Laurent. 2005 · 2005
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Robust reinforcement learning using adversarial populations
Vinitsky, Eugene, Du, Yuqing, Parvate, Kanaad, Jang, Kathy, Abbeel, Pieter, & Bayen, Alexandre. 2020 · 2008
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The Arcade Learning Environment: An Evaluation Platform for General Agents
Bellemare, Marc, Naddaf, Yavar, Veness, Joel, & Bowling, Michael. 2012 · 2012
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MuJoCo: A physics engine for model-based control
Todorov, Emanuel, Erez, Tom, & Tassa, Yuval. 2012 · 2012
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Robust Markov decision processes
Wiesemann, Wolfram, Kuhn, Daniel, & Rustem, Berç. 2013 · 2013
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Markov decision processes: discrete stochastic dynamic programming
Puterman, Martin L. 2014 · 2014
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DeepMind Lab
Beattie, Charles, Leibo, Joel Z., Teplyashin, Denis, Ward, Tom, Wainwright, Marcus, Küttler, Heinrich, Lefrancq, Andrew, Green, Simon, Valdés, Víctor, Sadik, Amir, Schrittwieser, Julian, Anderson, Keith, York, Sarah, Cant, Max, Cain, Adam, Bolton, Adrian, Gaffney, Stephen, King, Helen, Hassabis, Demis, Legg, Shane, & Petersen, Stig. 2016 · 2016
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OpenAI Gym
Brockman, Greg, Cheung, Vicki, Pettersson, Ludwig, Schneider, Jonas, Schulman, John, Tang, Jie, & Zaremba, Wojciech. 2016 · 2016
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Robust adversarial reinforcement learning
Pinto, Lerrel, Davidson, James, Sukthankar, Rahul, & Gupta, Abhinav. 2017 · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, Josh, Fong, Rachel, Ray, Alex, Schneider, Jonas, Zaremba, Wojciech, & Abbeel, Pieter. 2017 · 2017
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Soft Actor-Critic Algorithms and Applications
Haarnoja, Tuomas, Zhou, Aurick, Hartikainen, Kristian, Tucker, George, Ha, Sehoon, Tan, Jie, Kumar, Vikash, Zhu, Henry, Gupta, Abhishek, Abbeel, Pieter, & Levine, Sergey. 2018 · 2018
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Gotta learn fast: A new benchmark for generalization in rl
Nichol, Alex, Pfau, Vicki, Hesse, Christopher, Klimov, Oleg, & Schulman, John. 2018 · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Sutton, Richard S, & Barto, Andrew G. 2018 · 2018
Cited alongside, same era.
DeepMind Control Suite
Tassa, Yuval, Doron, Yotam, Muldal, Alistair, Erez, Tom, Li, Yazhe, de Las Casas, Diego, Budden, David, Abdolmaleki, Abbas, Merel, Josh, Lefrancq, Andrew, Lillicrap, Timothy, & Riedmiller, Martin. 2018 · 2018
Cited alongside, same era.
Policy Transfer with Strategy Optimization
Yu, Wenhao, Liu, C. K., & Turk, Greg. 2018 · 2018
Cited alongside, same era.
Benchmarking Safe Exploration in Deep Reinforcement Learning
Achiam, Joshua, & Amodei, Dario. 2019 · 2019
Cited alongside, same era.
Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient
Tactical optimism and pessimism for deep reinforcement learning
Moskovitz, Ted, Parker-Holder, Jack, Pacchiano, Aldo, Arbel, Michael, & Jordan, Michael. 2021 · 2021
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Robust Reinforcement Learning for Shifting Dynamics During Deployment
Stanton, Samuel, Fakoor, Rasool, Mueller, Jonas, Wilson, Andrew Gordon, & Smola, Alex. 2021 · 2021
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Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Yu, Tianhe, Quillen, Deirdre, He, Zhanpeng, Julian, Ryan, Narayan, Avnish, Shively, Hayden, Bellathur, Adithya, Hausman, Karol, Finn, Chelsea, & Levine, Sergey. 2021 · 2021
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Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
Zhang, Huan, Chen, Hongge, Boning, Duane S, & Hsieh, Cho-Jui. 2021 · 2021
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Robust reinforcement learning with distributional risk-averse formulation
Clavier, Pierre, Allassonière, Stéphanie, & Pennec, Erwan Le. 2022 · 2022
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Li, Shihui, Wu, Yi, Cui, Xinyue, Dong, Honghua, Fang, Fei, & Russell, Stuart. 2019 · 2019
Cited alongside, same era.
Action robust reinforcement learning and applications in continuous control
Tessler, Chen, Efroni, Yonathan, & Mannor, Shie. 2019 · 2019
Cited alongside, same era.
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Dennis, Michael, Jaques, Natasha, Vinitsky, Eugene, Bayen, A., Russell, Stuart J., Critch, Andrew, & Levine, S. 2020 · 2020
Cited alongside, same era.
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Gulcehre, Caglar, Wang, Ziyu, Novikov, Alexander, Paine, Tom Le, Colmenarejo, Sergio Gómez, Zolna, Konrad, Agarwal, Rishabh, Merel, Josh, Mankowitz, Daniel, Paduraru, Cosmin, Dulac-Arnold, Gabriel, Li, Jerry, Norouzi, Mohammad, Hoffman, Matt, Nachum, Ofir, Tucker, George, Heess, Nicolas, & deFreitas, Nando. 2020 · 2020
Cited alongside, same era.
RLBench: The Robot Learning Benchmark and Learning Environment
James, Stephen, Ma, Zicong, Arrojo, David Rovick, & Davison, Andrew J. 2020 · 2020
Cited alongside, same era.
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Kamalaruban, Parameswaran, ting Huang, Yu, Hsieh, Ya-Ping, Rolland, Paul, Shi, C., & Cevher, V. 2020 · 2020
Cited alongside, same era.
Model-based Adversarial Meta-Reinforcement Learning
Lin, Zichuan, Thomas, Garrett, Yang, Guangwen, & Ma, Tengyu. 2020 · 2020
Cited alongside, same era.
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CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
Huang, Shengyi, Dossa, Rousslan Fernand Julien, Ye, Chang, Braga, Jeff, Chakraborty, Dipam, Mehta, Kinal, & Araújo, João G.M. 2022 · 2022
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Efficient policy iteration for robust markov decision processes via regularization
Kumar, Navdeep, Levy, Kfir, Wang, Kaixin, & Mannor, Shie. 2022 · 2022
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Distributionally Robust Q Q -Learning
Liu, Zijian, Bai, Qinxun, Blanchet, Jose, Dong, Perry, Xu, Wei, Zhou, Zhengqing, & Zhou, Zhengyuan. 2022 · 2022
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Robust Reinforcement Learning: A Review of Foundations and Recent Advances
Moos, Janosch, Hansel, Kay, Abdulsamad, Hany, Stark, Svenja, Clever, Debora, & Peters, Jan. 2022 · 2022
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Sample complexity of robust reinforcement learning with a generative model
Panaganti, Kishan, & Kalathil, Dileep. 2022 · 2022
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Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
Tanabe, Takumi, Sato, Rei, Fukuchi, Kazuto, Sakuma, Jun, & Akimoto, Youhei. 2022 · 2022
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Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics
Yang, Wenhao, Zhang, Liangyu, & Zhang, Zhihua. 2022 · 2022
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Towards minimax optimality of model-based robust reinforcement learning
Clavier, Pierre, Pennec, Erwan Le, & Geist, Matthieu. 2023 · 2023
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Gymnasium
Towers, Mark, Terry, Jordan K., Kwiatkowski, Ariel, Balis, John U., Cola, Gianluca de, Deleu, Tristan, Goulão, Manuel, Kallinteris, Andreas, KG, Arjun, Krimmel, Markus, Perez-Vicente, Rodrigo, Pierré, Andrea, Schulhoff, Sander, Tai, Jun Jet, Shen, Andrew Tan Jin, & Younis, Omar G. 2023 (Mar.) · 2023
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Robust Reinforcement Learning via Adversarial Kernel Approximation
Wang, Kaixin, Gadot, Uri, Kumar, Navdeep, Levy, Kfir, & Mannor, Shie. 2023 · 2023
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The curious price of distributional robustness in reinforcement learning with a generative model
Shi, Laixi, Li, Gen, Wei, Yuting, Chen, Yuxin, Geist, Matthieu, & Chi, Yuejie. 2024 · 2024
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Leveraging procedural generation to benchmark reinforcement learning
Cobbe, Karl, Hesse, Chris, Hilton, Jacob, & Schulman, John. 2020 · 2056
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