Inverse Cooperative and Non-Cooperative Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learning. Article Swipe
Jairo Inga
,
Esther Bischoff
,
Florian Köpf
,
Michael Flad
,
Sören Hohmann
·
YOU?
·
· 2019
· Open Access
·
YOU?
·
· 2019
· Open Access
·
Dynamic game theory provides mathematical means for modeling the interaction between several players, where their decisions are explained by individual cost functions. The inverse problem of dynamic games, where cost functions are sought which explain observed behavior, has recently gained attention due to its potential application for identification of biological systems and the possibility of generalizing inverse optimal control results. In this paper, we extend maximum entropy inverse reinforcement learning to the N-player case in order to solve inverse dynamic games with continuous-valued state and control spaces. On this basis, we first present a method for identification of cost function parameters in a cooperative game. Afterwards, we propose an approach for identifying cost function parameters which explain the behavior of the players in a non-cooperative setting, i.e. open-loop and feedback Nash equilibrium behaviors. Furthermore, we give results on the unbiasedness of the estimation of cost function parameters for each class of inverse dynamic game. The applicability of the methods is demonstrated with simulation examples of a nonlinear and a linear-quadratic dynamic game.
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- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1911.07503.pdf
- OA Status
- green
- Cited By
- 3
- References
- 22
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2989169948
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https://openalex.org/W2989169948Canonical identifier for this work in OpenAlex
- Title
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Inverse Cooperative and Non-Cooperative Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learning.Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-11-18Full publication date if available
- Authors
-
Jairo Inga, Esther Bischoff, Florian Köpf, Michael Flad, Sören HohmannList of authors in order
- Landing page
-
https://arxiv.org/pdf/1911.07503.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1911.07503.pdfDirect OA link when available
- Concepts
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Inverse, Sequential game, Nash equilibrium, Mathematical optimization, Computer science, Reinforcement learning, Principle of maximum entropy, Mathematics, Inverse problem, Game theory, Applied mathematics, Mathematical economics, Artificial intelligence, Geometry, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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22Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mathematical | 4 |
| abstract_inverted_index.unbiasedness | 139 |
| abstract_inverted_index.applicability | 155 |
| abstract_inverted_index.reinforcement | 68 |
| abstract_inverted_index.identification | 47, 96 |
| abstract_inverted_index.non-cooperative | 124 |
| abstract_inverted_index.linear-quadratic | 169 |
| abstract_inverted_index.continuous-valued | 82 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |