Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach Article Swipe
Wenjian Hao
,
Yiqiang Han
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.09543
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.09543
This paper compares two different types of data-driven control methods, representing model-based and model-free approaches. One is a recently proposed method - Deep Koopman Representation for Control (DKRC), which utilizes a deep neural network to map an unknown nonlinear dynamical system to a high-dimensional linear system, which allows for employing state-of-the-art control strategy. The other one is a classic model-free control method based on an actor-critic architecture - Deep Deterministic Policy Gradient (DDPG), which has been proved to be effective in various dynamical systems. The comparison is carried out in OpenAI Gym, which provides multiple control environments for benchmark purposes. Two examples are provided for comparison, i.e., classic Inverted Pendulum and Lunar Lander Continuous Control. From the results of the experiments, we compare these two methods in terms of control strategies and the effectiveness under various initialization conditions. We also examine the learned dynamic model from DKRC with the analytical model derived from the Euler-Lagrange Linearization method, which demonstrates the accuracy in the learned model for unknown dynamics from a data-driven sample-efficient approach.
Related Topics
Concepts
Initialization
Computer science
Benchmark (surveying)
Representation (politics)
Linearization
Nonlinear system
Control theory (sociology)
Artificial neural network
System dynamics
Dynamical systems theory
Double pendulum
Pendulum
Dynamical system (definition)
Inverted pendulum
Control (management)
Artificial intelligence
Engineering
Physics
Geodesy
Political science
Mechanical engineering
Politics
Quantum mechanics
Law
Programming language
Geography
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2006.09543
- https://arxiv.org/pdf/2006.09543
- OA Status
- green
- Cited By
- 7
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3036624706
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3036624706Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2006.09543Digital Object Identifier
- Title
-
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free ApproachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-16Full publication date if available
- Authors
-
Wenjian Hao, Yiqiang HanList of authors in order
- Landing page
-
https://arxiv.org/abs/2006.09543Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2006.09543Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2006.09543Direct OA link when available
- Concepts
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Initialization, Computer science, Benchmark (surveying), Representation (politics), Linearization, Nonlinear system, Control theory (sociology), Artificial neural network, System dynamics, Dynamical systems theory, Double pendulum, Pendulum, Dynamical system (definition), Inverted pendulum, Control (management), Artificial intelligence, Engineering, Physics, Geodesy, Political science, Mechanical engineering, Politics, Quantum mechanics, Law, Programming language, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_year | 2020 |
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