Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains

Doshi-Velez, Finale

Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains

AAAI 2010

/aaai/2010/doshivelez2010aaai-nonparametric/

Abstract

The objective of my doctoral research is bring together two fields: partially-observable reinforcement learning (PORL) and non-parametric Bayesian statistics (NPB) to address issues of statistical modeling and decision-making in complex, real-world domains.

AAAI Semantic Scholar

Cite

Text

Doshi-Velez. "Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains." AAAI Conference on Artificial Intelligence, 2010.

Markdown

[Doshi-Velez. "Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/doshivelez2010aaai-nonparametric/)

BibTeX

@inproceedings{doshivelez2010aaai-nonparametric,
  title     = {{Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains}},
  author    = {Doshi-Velez, Finale},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2010},
  url       = {https://mlanthology.org/aaai/2010/doshivelez2010aaai-nonparametric/}
}