Robust Reinforcement Learning
Robust Reinforcement Learning is a reinforcement learning framework that extends standard Markov decision processes by explicitly accounting for uncertainties and adversarial perturbations in the environment dynamics or reward structure to ensure stable and reliable policy performance across diverse and non-stationary conditions.
210
Publications
11.4K
Citations
651
Authors
222
Institutions
Publications per year
2017–2026
172
Variants
Robust Rl
Parents
210
Robust Adversarial Reinforcement Learning
Lerrel Pinto, James Davidson, Rahul Sukthankar et al. · arXiv (Cornell University) · 2017 · 385 citations · Full text
Doubly Robust Policy Evaluation and Learning
John Langford, Lihong Li · arXiv (Cornell University) · 2011 · 303 citations · Full text
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222
Carnegie Mellon University
Pittsburgh, United States
12 h-index
18 publications
3.4K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
23 publications
1K citations
University of California, Berkeley
Berkeley, United States
10 h-index
26 publications
1.3K citations
Google (United States)
Mountain View, United States
9 h-index
16 publications
1.6K citations
University of Illinois Urbana-Champaign
Urbana, United States
9 h-index
23 publications
1.5K citations
Technion – Israel Institute of Technology
Haifa, Israel
8 h-index
15 publications
434 citations
Nanyang Technological University
Singapore, Singapore
7 h-index
21 publications
1.7K citations

University of Toronto
Toronto, Canada
6 h-index
11 publications
419 citations
1–8 of 222
Repository · 62 publications · 2.8K citations
26
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Conference · 11 publications · 638 citations
10
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4 publications · 291 citations
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4 publications · 101 citations
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4 publications · 127 citations
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