Image-based Regularization for Action Smoothness in Autonomous Miniature Racing Car with Deep Reinforcement Learning Article Swipe
Hoang-Giang Cao
,
Ickjai Lee
,
Bo-Jiun Hsu
,
Zhen-Xun Lee
,
Yu-Wei Shih
,
Hsueh‐Cheng Wang
,
I‐Chen Wu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.08230
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.08230
Deep reinforcement learning has achieved significant results in low-level controlling tasks. However, for some applications like autonomous driving and drone flying, it is difficult to control behavior stably since the agent may suddenly change its actions which often lowers the controlling system's efficiency, induces excessive mechanical wear, and causes uncontrollable, dangerous behavior to the vehicle. Recently, a method called conditioning for action policy smoothness (CAPS) was proposed to solve the problem of jerkiness in low-dimensional features for applications such as quadrotor drones. To cope with high-dimensional features, this paper proposes image-based regularization for action smoothness (I-RAS) for solving jerky control in autonomous miniature car racing. We also introduce a control based on impact ratio, an adaptive regularization weight to control the smoothness constraint, called IR control. In the experiment, an agent with I-RAS and IR control significantly improves the success rate from 59% to 95%. In the real-world-track experiment, the agent also outperforms other methods, namely reducing the average finish lap time, while also improving the completion rate even without real world training. This is also justified by an agent based on I-RAS winning the 2022 AWS DeepRacer Final Championship Cup.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.08230
- https://arxiv.org/pdf/2307.08230
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384644677
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384644677Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.08230Digital Object Identifier
- Title
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Image-based Regularization for Action Smoothness in Autonomous Miniature Racing Car with Deep Reinforcement LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-17Full publication date if available
- Authors
-
Hoang-Giang Cao, Ickjai Lee, Bo-Jiun Hsu, Zhen-Xun Lee, Yu-Wei Shih, Hsueh‐Cheng Wang, I‐Chen WuList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.08230Publisher landing page
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https://arxiv.org/pdf/2307.08230Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2307.08230Direct OA link when available
- Concepts
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Regularization (linguistics), Reinforcement learning, Computer science, Drone, Smoothness, Artificial intelligence, Action (physics), Computer vision, Mathematics, Genetics, Quantum mechanics, Biology, Mathematical analysis, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.excessive | 44 |
| abstract_inverted_index.features, | 86 |
| abstract_inverted_index.improving | 164 |
| abstract_inverted_index.introduce | 107 |
| abstract_inverted_index.jerkiness | 72 |
| abstract_inverted_index.justified | 176 |
| abstract_inverted_index.low-level | 8 |
| abstract_inverted_index.miniature | 102 |
| abstract_inverted_index.quadrotor | 80 |
| abstract_inverted_index.training. | 172 |
| abstract_inverted_index.autonomous | 16, 101 |
| abstract_inverted_index.completion | 166 |
| abstract_inverted_index.mechanical | 45 |
| abstract_inverted_index.smoothness | 63, 94, 121 |
| abstract_inverted_index.constraint, | 122 |
| abstract_inverted_index.controlling | 9, 40 |
| abstract_inverted_index.efficiency, | 42 |
| abstract_inverted_index.experiment, | 128, 148 |
| abstract_inverted_index.image-based | 90 |
| abstract_inverted_index.outperforms | 152 |
| abstract_inverted_index.significant | 5 |
| abstract_inverted_index.Championship | 189 |
| abstract_inverted_index.applications | 14, 77 |
| abstract_inverted_index.conditioning | 59 |
| abstract_inverted_index.reinforcement | 1 |
| abstract_inverted_index.significantly | 136 |
| abstract_inverted_index.regularization | 91, 116 |
| abstract_inverted_index.low-dimensional | 74 |
| abstract_inverted_index.uncontrollable, | 49 |
| abstract_inverted_index.high-dimensional | 85 |
| abstract_inverted_index.real-world-track | 147 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |