Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains Article Swipe
Florian Köpf
,
Alexander Nitsch
,
Michael Flad
,
Sören Hohmann
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/iccar49639.2020.9108080
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/iccar49639.2020.9108080
Mixed cooperative-competitive control scenarios such as human-machine interaction with individual goals of the interacting partners are very challenging for reinforcement learning agents. In order to contribute towards intuitive human-machine collaboration, we focus on problems in the continuous state and control domain where no explicit communication is considered and the agents do not know the others' goals or control laws but only sense their control inputs retrospectively. Our proposed framework combines a learned partner model based on online data with a reinforcement learning agent that is trained in a simulated environment including the partner model. Thus, we overcome drawbacks of independent learners and, in addition, benefit from a reduced amount of real world data required for reinforcement learning which is vital in the human-machine context. We finally analyze an example that demonstrates the merits of our proposed framework which learns fast due to the simulated environment and adapts to the continuously changing partner due to the partner approximation.
Related Topics
Concepts
Reinforcement learning
Computer science
Context (archaeology)
Control (management)
Domain (mathematical analysis)
Artificial intelligence
Focus (optics)
State (computer science)
Error-driven learning
Human–computer interaction
Machine learning
Algorithm
Mathematical analysis
Paleontology
Physics
Optics
Biology
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/iccar49639.2020.9108080
- OA Status
- green
- Cited By
- 1
- References
- 34
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2971664666
All OpenAlex metadata
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https://openalex.org/W2971664666Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/iccar49639.2020.9108080Digital Object Identifier
- Title
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Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent DomainsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-04-01Full publication date if available
- Authors
-
Florian Köpf, Alexander Nitsch, Michael Flad, Sören HohmannList of authors in order
- Landing page
-
https://doi.org/10.1109/iccar49639.2020.9108080Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1909.03868.pdfDirect OA link when available
- Concepts
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Reinforcement learning, Computer science, Context (archaeology), Control (management), Domain (mathematical analysis), Artificial intelligence, Focus (optics), State (computer science), Error-driven learning, Human–computer interaction, Machine learning, Algorithm, Mathematical analysis, Paleontology, Physics, Optics, Biology, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2020: 1Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6713411898, https://openalex.org/W2768629321, https://openalex.org/W6748910126, https://openalex.org/W6744562401, https://openalex.org/W6684921986, https://openalex.org/W6616173779, https://openalex.org/W2606397423, https://openalex.org/W2041521369, https://openalex.org/W6770858630, https://openalex.org/W6696324988, https://openalex.org/W1980035368, https://openalex.org/W2108892923, https://openalex.org/W6741002519, https://openalex.org/W2096145798, https://openalex.org/W6738796088, https://openalex.org/W6712181171, https://openalex.org/W1982262386, https://openalex.org/W6746809867, https://openalex.org/W6780559895, https://openalex.org/W2173248099, https://openalex.org/W2964338167, https://openalex.org/W2990747716, https://openalex.org/W2623431351, https://openalex.org/W2606342131, https://openalex.org/W1757796397, https://openalex.org/W2736601468, https://openalex.org/W2963407617, https://openalex.org/W2772721022, https://openalex.org/W2290354866, https://openalex.org/W2395575420, https://openalex.org/W567721252, https://openalex.org/W2963000099, https://openalex.org/W2963627051, https://openalex.org/W2788212683 |
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