Counterfactual state explanations for reinforcement learning agents via generative deep learning
• Deep learning models can generate counterfactual states for game-playing agents. • Generative counterfactual states are sufficiently realistic according to humans. • Counterfactual states can be used for identifying a flawed agent. • Generated counterfactual states outperform nearest neighbors in...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Olson, Matthew L. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The association between hip strength, physical function and dynamic balance in people with unilateral knee osteoarthritis: A cross-sectional study - Hislop, Andrew ELSEVIER, 2022, Amsterdam |
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| Übergeordnetes Werk: |
volume:295 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.artint.2021.103455 |
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Counterfactual state explanations for reinforcement learning agents via generative deep learning |
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• Deep learning models can generate counterfactual states for game-playing agents. • Generative counterfactual states are sufficiently realistic according to humans. • Counterfactual states can be used for identifying a flawed agent. • Generated counterfactual states outperform nearest neighbors in flaw detection. |
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• Deep learning models can generate counterfactual states for game-playing agents. • Generative counterfactual states are sufficiently realistic according to humans. • Counterfactual states can be used for identifying a flawed agent. • Generated counterfactual states outperform nearest neighbors in flaw detection. |
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• Deep learning models can generate counterfactual states for game-playing agents. • Generative counterfactual states are sufficiently realistic according to humans. • Counterfactual states can be used for identifying a flawed agent. • Generated counterfactual states outperform nearest neighbors in flaw detection. |
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