# Learning rewards from exploratory demonstrations using probabilistic temporal ranking

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/learning-rewards-from-exploratory-demonstrations-using-probabilistic-temporal/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Michael Burke,Katie Lu,Daniel Angelov,Artūras Straižys,Craig Innes,Kartic Subr,Subramanian Ramamoorthy |
| Citations | 15 |
| DOI | 10.1007/s10514-023-10120-w |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://link.springer.com/content/pdf/10.1007/s10514-023-10120-w.pdf |
| OpenAlex ID | https://openalex.org/W4383755721 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Artūras Straižys](https://scholariq.org/researchers/arturas-straizys/)

## Paper primary topic

- [Robotics and Sensor-Based Localization](https://scholariq.org/topics/robotics-and-sensor-based-localization/)

## Paper topics

- [Robotics and Sensor-Based Localization](https://scholariq.org/topics/robotics-and-sensor-based-localization/)
- [Advanced Vision and Imaging](https://scholariq.org/topics/advanced-vision-and-imaging/)
- [Domain Adaptation and Few-Shot Learning](https://scholariq.org/topics/domain-adaptation-and-few-shot-learning/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.