active-preference-based-gpr

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active-preference-based-gpr

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Companion code for RSS 2020 paper: "Active Preference-Based Gaussian Process Regression for Reward Learning"

active-preference-based-gpr 최신버젼 다운로드

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// repository documentation

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This code performs preference-based GP regression, inference and active query generation.

Companion code to RSS 2020 paper:
E Bıyık\*, N Huynh\*, MJ Kochenderfer, D Sadigh, "Active Preference-Based Gaussian Process Regression for Reward Learning", Proceedings of Robotics: Science and Systems (RSS), Corvallis, Oregon, USA, Jul. 2020.

## Dependencies
You need to have the following libraries with [Python3](http://www.python.org/downloads):
- [NumPy](https://www.numpy.org/)
- [SciPy](https://www.scipy.org/)

## Running
You simply read test.py to understand how to use the package. For testing, just run
```python
	python test.py
```

## Paper citation
If you used this code or found it helpful, consider citing the following paper:

<pre>
@inproceedings{biyik2020active,
  title={Active Preference-Based Gaussian Process Regression for Reward Learning},
  author={Biyik, Erdem and Huynh, Nicolas and Kochenderfer, Mykel J. and Sadigh, Dorsa},
  booktitle={Proceedings of Robotics: Science and Systems (RSS)},
  year={2020},
  month={July}
}
</pre>

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