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Robustness has been extensively studied in reinforcement learning (RL) to handle various forms of uncertainty such as random perturbations, rare events, and malicious attacks.
Bisimulation through probabilistic testing (preliminary report)
Kim G Larsen and Arne Skou · 1989
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Robust dynamic programming
Garud N Iyengar · 2005
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Causality
Judea Pearl · 2009
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Distributionally robust markov decision processes
Huan Xu and Shie Mannor · 2010
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Robust control of uncertain markov decision processes with temporal logic specifications
Eric M Wolff, Ufuk Topcu, and Richard M Murray · 2012
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Robust markov decision processes
Wolfram Wiesemann, Daniel Kuhn, and Berç Rustem · 2013
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Robust modified policy iteration
David L Kaufman and Andrew J Schaefer · 2013
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Scaling up robust mdps using function approximation
Aviv Tamar, Shie Mannor, and Huan Xu · 2014
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Contextual markov decision processes
Assaf Hallak, Dotan Di Castro, and Shie Mannor · 2015
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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, Veda Panneershelvam, Marc Lanctot, et al · 2016
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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
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Benchmarking reinforcement learning algorithms on real-world robots
A Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, and James Bergstra · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Hierarchical imitation and reinforcement learning
Hoang Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, and Hal Daumé III · 2018
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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
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Fast bellman updates for robust mdps
Chin Pang Ho, Marek Petrik, and Wolfram Wiesemann · 2018
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Nataniel Ruiz, Samuel Schulter, and Manmohan Chandraker · 2018
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Causal confusion in imitation learning
Pim De Haan, Dinesh Jayaraman, and Sergey Levine · 2019
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Action robust reinforcement learning and applications in continuous control
Chen Tessler, Yonathan Efroni, and Shie Mannor · 2019
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A theory of state abstraction for reinforcement learning
David Abel · 2019
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Distributionally robust reinforcement learning
Elena Smirnova, Elvis Dohmatob, and Jérémie Mary · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2019
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan Ratliff, and Dieter Fox · 2019
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Deceptionnet: Network-driven domain randomization
Sergey Zakharov, Wadim Kehl, and Slobodan Ilic · 2019
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Don’t take the easy way out: Ensemble-based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer · 2019
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton · 2019
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Reinforcement learning: Theory and algorithms
Alekh Agarwal, Nan Jiang, Sham M Kakade, and Wen Sun · 2019
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Robust deep reinforcement learning against adversarial perturbations on state observations
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane Boning, and Cho-Jui Hsieh · 2020
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Robustifying reinforcement learning agents via action space adversarial training
Kai Liang Tan, Yasaman Esfandiari, Xian Yeow Lee, and Soumik Sarkar · 2020
Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2020
Cited alongside, same era.
Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
Cited alongside, same era.
Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2020
Cited alongside, same era.
Distributional robustness and regularization in reinforcement learning
Robust reinforcement learning: A review of foundations and recent advances
Janosch Moos, Kay Hansel, Hany Abdulsamad, Svenja Stark, Debora Clever, and Jan Peters · 2022
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What is the solution for state adversarial multi-agent reinforcement learning?
Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, and Fei Miao · 2022
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Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics
Wenhao Yang, Liangyu Zhang, and Zhihua Zhang · 2022
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Laixi Shi and Yuejie Chi · 2022
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Esther Derman and Shie Mannor · 2020
Cited alongside, same era.
Improving generalization in reinforcement learning with mixture regularization
Kaixin Wang, Bingyi Kang, Jie Shao, and Jiashi Feng · 2020
Cited alongside, same era.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
Cited alongside, same era.
Sample-efficient reinforcement learning via counterfactual-based data augmentation
Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, and Bernhard Schölkopf · 2020
Cited alongside, same era.
Counterfactual data augmentation using locally factored dynamics
Silviu Pitis, Elliot Creager, and Animesh Garg · 2020
Cited alongside, same era.
Active domain randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J Pal, and Liam Paull · 2020
Cited alongside, same era.
No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré · 2020
Cited alongside, same era.
Debiasing concept-based explanations with causal analysis
Mohammad Taha Bahadori and David E Heckerman · 2020
Cited alongside, same era.
Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, and Linxi Fan · 2022
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Skills regularized task decomposition for multi-task offline reinforcement learning
Minjong Yoo, Sangwoo Cho, and Honguk Woo · 2022
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Mocoda: Model-based counterfactual data augmentation
Silviu Pitis, Elliot Creager, Ajay Mandlekar, and Animesh Garg · 2022
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Robust markov decision processes: Beyond rectangularity
Vineet Goyal and Julien Grand-Clement · 2022
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Online policy optimization for robust mdp
Jing Dong, Jingwei Li, Baoxiang Wang, and Jingzhao Zhang · 2022
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Sample complexity of robust reinforcement learning with a generative model
Kishan Panaganti and Dileep Kalathil · 2022
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Distributionally robust q q -learning
Zijian Liu, Qinxun Bai, Jose Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, and Zhengyuan Zhou · 2022
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Robust reinforcement learning using offline data
Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, and Mohammad Ghavamzadeh · 2022
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Distributionally robust offline reinforcement learning with linear function approximation
Xiaoteng Ma, Zhipeng Liang, Jose Blanchet, Mingwen Liu, Li Xia, Jiheng Zhang, Qianchuan Zhao, and Zhengyuan Zhou · 2022
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Defending observation attacks in deep reinforcement learning via detection and denoising
Zikang Xiong, Joe Eappen, He Zhu, and Suresh Jagannathan · 2022
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Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations
Michael Zhang, Nimit S Sohoni, Hongyang R Zhang, Chelsea Finn, and Christopher Ré · 2022
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Robust policy learning over multiple uncertainty sets
Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, and Amy Zhang · 2022
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Unsupervised learning of debiased representations with pseudo-attributes
Seonguk Seo, Joon-Young Lee, and Bohyung Han · 2022
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Identifying spurious correlations and correcting them with an explanation-based learning
Misgina Tsighe Hagos, Kathleen M Curran, and Brian Mac Namee · 2022
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Meaningfully debugging model mistakes using conceptual counterfactual explanations
Abubakar Abid, Mert Yuksekgonul, and James Zou · 2022
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Concept-level debugging of part-prototype networks
Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, and Andrea Passerini · 2022
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Tianshou: A highly modularized deep reinforcement learning library
Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, and Jun Zhu · 2022
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Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
Group distributionally robust reinforcement learning with hierarchical latent variables
Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, et al · 2023
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Can active sampling reduce causal confusion in offline reinforcement learning?
Gunshi Gupta, Tim GJ Rudner, Rowan Thomas McAllister, Adrien Gaidon, and Yarin Gal · 2023
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Discover and cure: Concept-aware mitigation of spurious correlation
Shirley Wu, Mert Yuksekgonul, Linjun Zhang, and James Zou · 2023
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Improved sample complexity bounds for distributionally robust reinforcement learning
Zaiyan Xu, Kishan Panaganti, and Dileep Kalathil · 2023
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A finite sample complexity bound for distributionally robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet, and Zhengyuan Zhou · 2023
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Jose Blanchet, Miao Lu, Tong Zhang, and Han Zhong · 2023
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Sample complexity of variance-reduced distributionally robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet, and Zhengyuan Zhou · 2023
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Single-trajectory distributionally robust reinforcement learning
Zhipeng Liang, Xiaoteng Ma, Jose Blanchet, Jiheng Zhang, and Zhengyuan Zhou · 2023
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The curious price of distributional robustness in reinforcement learning with a generative model
Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Matthieu Geist, and Yuejie Chi · 2023
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Distributionally robust model-based reinforcement learning with large state spaces
Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, and Ilija Bogunovic · 2023
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Synthesizing adversarial visual scenarios for model-based robotic control
Shubhankar Agarwal and Sandeep P Chinchali · 2023
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