Deep reinforcement learning algorithm for dynamic pricing of express lanes with multiple access locations
• Dynamic pricing formulated as a partially observable Markov decision process. • Deep reinforcement learning (Deep-RL) algorithms used as solution methods. • Deep-RL algorithms outperform feedback control heuristic on different objectives. • Policies trained using Deep-RL algorithms transfer well t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pandey, Venktesh [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Dithiocarbamate-calix[4]arene functionalized gold nanoparticles as a selective and sensitive colorimetric probe for assay of metsulfuron-methyl herbicide - Rohit, Jigneshkumar V. ELSEVIER, 2016, an international journal : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:119 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.trc.2020.102715 |
|---|
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ELV051489449 |
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| 520 | |a • Dynamic pricing formulated as a partially observable Markov decision process. • Deep reinforcement learning (Deep-RL) algorithms used as solution methods. • Deep-RL algorithms outperform feedback control heuristic on different objectives. • Policies trained using Deep-RL algorithms transfer well to new input distributions. • Deep-RL algorithms suitable for multi-objective optimization using reward shaping. | ||
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