# Reinforcement Learning in Robotics

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/reinforcement-learning-in-robotics-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 769,187 |
| Description | This cluster of papers encompasses a wide range of advancements in reinforcement learning algorithms and their applications, including deep learning, neural networks, robotics, autonomous control, policy gradient methods, multi-agent systems, model-based learning, curiosity-driven exploration, and simulation to real-world transfer. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T10462 |
| Works | 61,453 |

## Topic researchers

Showing 12 of 20.

- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Geoffrey E. Hinton](https://scholariq.org/researchers/geoffrey-e-hinton/)
- [Yann LeCun](https://scholariq.org/researchers/yann-lecun/)
- [Li Fei-Fei](https://scholariq.org/researchers/li-fei-fei/)
- [Ilya Sutskever](https://scholariq.org/researchers/ilya-sutskever/)
- [David Haussler](https://scholariq.org/researchers/david-haussler/)
- [Trevor Darrell](https://scholariq.org/researchers/trevor-darrell/)
- [Frede Blaabjerg](https://scholariq.org/researchers/frede-blaabjerg/)
- [Demis Hassabis](https://scholariq.org/researchers/demis-hassabis/)
- [Jürgen Schmidhuber](https://scholariq.org/researchers/jurgen-schmidhuber/)
- [Michael I. Jordan](https://scholariq.org/researchers/michael-i-jordan/)
- [Oriol Vinyals](https://scholariq.org/researchers/oriol-vinyals/)

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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.