Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations Article Swipe
Zhuangdi Zhu
,
Kaixiang Lin
,
Bo Dai
,
Jiayu Zhou
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.00530
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.00530
Model-free deep reinforcement learning (RL) has demonstrated its superiority on many complex sequential decision-making problems. However, heavy dependence on dense rewards and high sample-complexity impedes the wide adoption of these methods in real-world scenarios. On the other hand, imitation learning (IL) learns effectively in sparse-rewarded tasks by leveraging the existing expert demonstrations. In practice, collecting a sufficient amount of expert demonstrations can be prohibitively expensive, and the quality of demonstrations typically limits the performance of the learning policy. In this work, we propose Self-Adaptive Imitation Learning (SAIL) that can achieve (near) optimal performance given only a limited number of sub-optimal demonstrations for highly challenging sparse reward tasks. SAIL bridges the advantages of IL and RL to reduce the sample complexity substantially, by effectively exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the demonstrated performance. Extensive empirical results show that not only does SAIL significantly improve the sample-efficiency but also leads to much better final performance across different continuous control tasks, comparing to the state-of-the-art.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2004.00530
- https://arxiv.org/pdf/2004.00530
- OA Status
- green
- Cited By
- 11
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3015024584
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3015024584Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2004.00530Digital Object Identifier
- Title
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Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-04-01Full publication date if available
- Authors
-
Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu ZhouList of authors in order
- Landing page
-
https://arxiv.org/abs/2004.00530Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2004.00530Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2004.00530Direct OA link when available
- Concepts
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Computer science, Reinforcement learning, Sample complexity, Sample (material), Machine learning, Artificial intelligence, Imitation, Quality (philosophy), Epistemology, Chromatography, Chemistry, Social psychology, Philosophy, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 1, 2021: 4, 2020: 2Per-year citation counts (last 5 years)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.final | 155 |
| abstract_inverted_index.given | 93 |
| abstract_inverted_index.hand, | 37 |
| abstract_inverted_index.heavy | 16 |
| abstract_inverted_index.leads | 151 |
| abstract_inverted_index.other | 36 |
| abstract_inverted_index.tasks | 45 |
| abstract_inverted_index.these | 29 |
| abstract_inverted_index.work, | 80 |
| abstract_inverted_index.(SAIL) | 86 |
| abstract_inverted_index.(near) | 90 |
| abstract_inverted_index.across | 157 |
| abstract_inverted_index.amount | 57 |
| abstract_inverted_index.better | 154 |
| abstract_inverted_index.expert | 50, 59 |
| abstract_inverted_index.highly | 102 |
| abstract_inverted_index.learns | 41 |
| abstract_inverted_index.limits | 71 |
| abstract_inverted_index.number | 97 |
| abstract_inverted_index.reduce | 116 |
| abstract_inverted_index.reward | 105 |
| abstract_inverted_index.sample | 118 |
| abstract_inverted_index.sparse | 104 |
| abstract_inverted_index.tasks, | 161 |
| abstract_inverted_index.tasks. | 106 |
| abstract_inverted_index.achieve | 89 |
| abstract_inverted_index.bridges | 108 |
| abstract_inverted_index.complex | 11 |
| abstract_inverted_index.control | 160 |
| abstract_inverted_index.impedes | 24 |
| abstract_inverted_index.improve | 146 |
| abstract_inverted_index.limited | 96 |
| abstract_inverted_index.methods | 30 |
| abstract_inverted_index.optimal | 91 |
| abstract_inverted_index.policy. | 77 |
| abstract_inverted_index.propose | 82 |
| abstract_inverted_index.quality | 67 |
| abstract_inverted_index.results | 138 |
| abstract_inverted_index.rewards | 20 |
| abstract_inverted_index.surpass | 132 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.Learning | 85 |
| abstract_inverted_index.adoption | 27 |
| abstract_inverted_index.existing | 49 |
| abstract_inverted_index.learning | 3, 39, 76 |
| abstract_inverted_index.Extensive | 136 |
| abstract_inverted_index.Imitation | 84 |
| abstract_inverted_index.comparing | 162 |
| abstract_inverted_index.different | 158 |
| abstract_inverted_index.empirical | 137 |
| abstract_inverted_index.exploring | 128 |
| abstract_inverted_index.imitation | 38 |
| abstract_inverted_index.practice, | 53 |
| abstract_inverted_index.problems. | 14 |
| abstract_inverted_index.typically | 70 |
| abstract_inverted_index.Model-free | 0 |
| abstract_inverted_index.advantages | 110 |
| abstract_inverted_index.collecting | 54 |
| abstract_inverted_index.complexity | 119 |
| abstract_inverted_index.continuous | 159 |
| abstract_inverted_index.dependence | 17 |
| abstract_inverted_index.expensive, | 64 |
| abstract_inverted_index.exploiting | 123 |
| abstract_inverted_index.leveraging | 47 |
| abstract_inverted_index.real-world | 32 |
| abstract_inverted_index.scenarios. | 33 |
| abstract_inverted_index.sequential | 12 |
| abstract_inverted_index.sufficient | 56 |
| abstract_inverted_index.challenging | 103 |
| abstract_inverted_index.effectively | 42, 122 |
| abstract_inverted_index.efficiently | 127 |
| abstract_inverted_index.environment | 130 |
| abstract_inverted_index.performance | 73, 92, 156 |
| abstract_inverted_index.sub-optimal | 99 |
| abstract_inverted_index.sup-optimal | 124 |
| abstract_inverted_index.superiority | 8 |
| abstract_inverted_index.demonstrated | 6, 134 |
| abstract_inverted_index.performance. | 135 |
| abstract_inverted_index.Self-Adaptive | 83 |
| abstract_inverted_index.prohibitively | 63 |
| abstract_inverted_index.reinforcement | 2 |
| abstract_inverted_index.significantly | 145 |
| abstract_inverted_index.demonstrations | 60, 69, 100, 125 |
| abstract_inverted_index.substantially, | 120 |
| abstract_inverted_index.decision-making | 13 |
| abstract_inverted_index.demonstrations. | 51 |
| abstract_inverted_index.sparse-rewarded | 44 |
| abstract_inverted_index.sample-complexity | 23 |
| abstract_inverted_index.sample-efficiency | 148 |
| abstract_inverted_index.state-of-the-art. | 165 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |