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From Semantics to Execution: Integrating Action Planning With Reinforcement Learning for Robotic Causal Problem-Solving
Reinforcement learning is generally accepted to be an appropriate and successful method to learn robot control. Symbolic action planning is useful to resolve causal dependencies and to break a causally complex problem down into a sequence of simpler high-level actions. A problem with the integration...
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| Veröffentlicht in: | Front Robot AI |
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| Hauptverfasser: | , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Frontiers Media S.A.
2019
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7805615/ https://ncbi.nlm.nih.gov/pubmed/33501138 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/frobt.2019.00123 |
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