The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model Article Swipe
Laixi Shi
,
Gen Li
,
Yuting Wei
,
Yuxin Chen
,
Matthieu Geist
,
Yuejie Chi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.16589
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.16589
This paper investigates model robustness in reinforcement learning (RL) to reduce the sim-to-real gap in practice. We adopt the framework of distributionally robust Markov decision processes (RMDPs), aimed at learning a policy that optimizes the worst-case performance when the deployed environment falls within a prescribed uncertainty set around the nominal MDP. Despite recent efforts, the sample complexity of RMDPs remained mostly unsettled regardless of the uncertainty set in use. It was unclear if distributional robustness bears any statistical consequences when benchmarked against standard RL. Assuming access to a generative model that draws samples based on the nominal MDP, we provide a near-optimal characterization of the sample complexity of RMDPs when the uncertainty set is specified via either the total variation (TV) distance or chi-squared divergence. The algorithm studied here is a model-based method called distributionally robust value iteration, which is shown to be near-optimal for the full range of uncertainty levels. Somewhat surprisingly, our results uncover that RMDPs are not necessarily easier or harder to learn than standard MDPs. The statistical consequence incurred by the robustness requirement depends heavily on the size and shape of the uncertainty set: in the case w.r.t.~the TV distance, the minimax sample complexity of RMDPs is always smaller than that of standard MDPs; in the case w.r.t.~the chi-squared divergence, the sample complexity of RMDPs far exceeds the standard MDP counterpart.
Related Topics
Concepts
Robustness (evolution)
Reinforcement learning
Minimax
Markov decision process
Sample complexity
Computer science
Mathematical optimization
Sample size determination
Generative model
Divergence (linguistics)
Markov process
Generative grammar
Mathematics
Artificial intelligence
Statistics
Biochemistry
Chemistry
Philosophy
Linguistics
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.16589
- https://arxiv.org/pdf/2305.16589
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378713459
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378713459Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2305.16589Digital Object Identifier
- Title
-
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative ModelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-26Full publication date if available
- Authors
-
Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Matthieu Geist, Yuejie ChiList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.16589Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.16589Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2305.16589Direct OA link when available
- Concepts
-
Robustness (evolution), Reinforcement learning, Minimax, Markov decision process, Sample complexity, Computer science, Mathematical optimization, Sample size determination, Generative model, Divergence (linguistics), Markov process, Generative grammar, Mathematics, Artificial intelligence, Statistics, Biochemistry, Chemistry, Philosophy, Linguistics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.adopt | 17 |
| abstract_inverted_index.aimed | 27 |
| abstract_inverted_index.based | 93 |
| abstract_inverted_index.bears | 75 |
| abstract_inverted_index.draws | 91 |
| abstract_inverted_index.falls | 41 |
| abstract_inverted_index.learn | 165 |
| abstract_inverted_index.model | 3, 89 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.range | 147 |
| abstract_inverted_index.shape | 183 |
| abstract_inverted_index.shown | 140 |
| abstract_inverted_index.total | 118 |
| abstract_inverted_index.value | 136 |
| abstract_inverted_index.which | 138 |
| abstract_inverted_index.Markov | 23 |
| abstract_inverted_index.access | 85 |
| abstract_inverted_index.always | 201 |
| abstract_inverted_index.around | 47 |
| abstract_inverted_index.called | 133 |
| abstract_inverted_index.easier | 161 |
| abstract_inverted_index.either | 116 |
| abstract_inverted_index.harder | 163 |
| abstract_inverted_index.method | 132 |
| abstract_inverted_index.mostly | 60 |
| abstract_inverted_index.policy | 31 |
| abstract_inverted_index.recent | 52 |
| abstract_inverted_index.reduce | 10 |
| abstract_inverted_index.robust | 22, 135 |
| abstract_inverted_index.sample | 55, 105, 196, 215 |
| abstract_inverted_index.within | 42 |
| abstract_inverted_index.Despite | 51 |
| abstract_inverted_index.against | 81 |
| abstract_inverted_index.depends | 177 |
| abstract_inverted_index.exceeds | 220 |
| abstract_inverted_index.heavily | 178 |
| abstract_inverted_index.levels. | 150 |
| abstract_inverted_index.minimax | 195 |
| abstract_inverted_index.nominal | 49, 96 |
| abstract_inverted_index.provide | 99 |
| abstract_inverted_index.results | 154 |
| abstract_inverted_index.samples | 92 |
| abstract_inverted_index.smaller | 202 |
| abstract_inverted_index.studied | 127 |
| abstract_inverted_index.unclear | 71 |
| abstract_inverted_index.uncover | 155 |
| abstract_inverted_index.(RMDPs), | 26 |
| abstract_inverted_index.Assuming | 84 |
| abstract_inverted_index.Somewhat | 151 |
| abstract_inverted_index.decision | 24 |
| abstract_inverted_index.deployed | 39 |
| abstract_inverted_index.distance | 121 |
| abstract_inverted_index.efforts, | 53 |
| abstract_inverted_index.incurred | 172 |
| abstract_inverted_index.learning | 7, 29 |
| abstract_inverted_index.remained | 59 |
| abstract_inverted_index.standard | 82, 167, 206, 222 |
| abstract_inverted_index.algorithm | 126 |
| abstract_inverted_index.distance, | 193 |
| abstract_inverted_index.framework | 19 |
| abstract_inverted_index.optimizes | 33 |
| abstract_inverted_index.practice. | 15 |
| abstract_inverted_index.processes | 25 |
| abstract_inverted_index.specified | 114 |
| abstract_inverted_index.unsettled | 61 |
| abstract_inverted_index.variation | 119 |
| abstract_inverted_index.complexity | 56, 106, 197, 216 |
| abstract_inverted_index.generative | 88 |
| abstract_inverted_index.iteration, | 137 |
| abstract_inverted_index.prescribed | 44 |
| abstract_inverted_index.regardless | 62 |
| abstract_inverted_index.robustness | 4, 74, 175 |
| abstract_inverted_index.w.r.t.~the | 191, 211 |
| abstract_inverted_index.worst-case | 35 |
| abstract_inverted_index.benchmarked | 80 |
| abstract_inverted_index.chi-squared | 123, 212 |
| abstract_inverted_index.consequence | 171 |
| abstract_inverted_index.divergence, | 213 |
| abstract_inverted_index.divergence. | 124 |
| abstract_inverted_index.environment | 40 |
| abstract_inverted_index.model-based | 131 |
| abstract_inverted_index.necessarily | 160 |
| abstract_inverted_index.performance | 36 |
| abstract_inverted_index.requirement | 176 |
| abstract_inverted_index.sim-to-real | 12 |
| abstract_inverted_index.statistical | 77, 170 |
| abstract_inverted_index.uncertainty | 45, 65, 111, 149, 186 |
| abstract_inverted_index.consequences | 78 |
| abstract_inverted_index.counterpart. | 224 |
| abstract_inverted_index.investigates | 2 |
| abstract_inverted_index.near-optimal | 101, 143 |
| abstract_inverted_index.reinforcement | 6 |
| abstract_inverted_index.surprisingly, | 152 |
| abstract_inverted_index.distributional | 73 |
| abstract_inverted_index.characterization | 102 |
| abstract_inverted_index.distributionally | 21, 134 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |