Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning Article Swipe
Zhiqian Qiao
,
Zachariah Tyree
,
Priyantha Mudalige
,
Jeff Schneider
,
John M. Dolan
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.03799
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.03799
In this work, we propose a hierarchical reinforcement learning (HRL) structure which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals. In this hierarchical structure, the network is capable of 1) learning one task with multiple sub-goals simultaneously; 2) extracting attentions of states according to changing sub-goals during the learning process; 3) reusing the well-trained network of sub-goals for other similar tasks with the same sub-goals. The states are defined as processed observations which are transmitted from the perception system of the autonomous vehicle. A hybrid reward mechanism is designed for different hierarchical layers in the proposed HRL structure. Compared to traditional RL methods, our algorithm is more sample-efficient since its modular design allows reusing the policies of sub-goals across similar tasks. The results show that the proposed method converges to an optimal policy faster than traditional RL methods.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1911.03799
- https://arxiv.org/pdf/1911.03799
- OA Status
- green
- Cited By
- 7
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2986034419
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https://openalex.org/W2986034419Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1911.03799Digital Object Identifier
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Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior PlanningWork title
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preprintOpenAlex work type
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enPrimary language
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2019Year of publication
- Publication date
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2019-11-09Full publication date if available
- Authors
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Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. DolanList of authors in order
- Landing page
-
https://arxiv.org/abs/1911.03799Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1911.03799Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1911.03799Direct OA link when available
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Reinforcement learning, Modular design, Computer science, Reuse, Task (project management), Process (computing), Artificial intelligence, Perception, Machine learning, Engineering, Operating system, Neuroscience, Waste management, Systems engineering, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2024: 1, 2023: 1, 2021: 3, 2020: 2Per-year citation counts (last 5 years)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.environments | 22 |
| abstract_inverted_index.hierarchical | 6, 28, 97 |
| abstract_inverted_index.observations | 77 |
| abstract_inverted_index.well-trained | 59 |
| abstract_inverted_index.reinforcement | 7 |
| abstract_inverted_index.simultaneously; | 42 |
| abstract_inverted_index.sample-efficient | 113 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |