Towards More Practical Reinforcement Learning

Mandel, Travis

Towards More Practical Reinforcement Learning

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4381-4381.

Abstract

Reinforcement Learning is beginning to be applied outside traditional domains such as robotics, and into human-centric domains such as healthcare and education. In these domains, two problems are critical to address: We must be able to evaluate algorithms with a collection of prior data if one is available, and we must devise algorithms that carefully trade off exploration and exploitation in such a way that they are guaranteed to converge to optimal behavior quickly, while retaining very good performance with limited data. In this thesis, I examine these two problems, with an eye towards applications to educational games.

Travis Mandel. 2015. Towards More Practical Reinforcement Learning. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 4381–4381. https://ijcai.org/proceedings/2015/638

@inproceedings{ijcai2015p638,
  title     = {Towards More Practical Reinforcement Learning},
  author    = {Mandel, Travis},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-15}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Qiang Yang and Michael Wooldridge},
  pages     = {4381--4381},
  year      = {2015},
  month     = {7},
  note      = {Doctoral Consortium},
  url       = {https://ijcai.org/proceedings/2015/638},
}

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TI  - Towards More Practical Reinforcement Learning
AU  - Mandel, Travis
T2  - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15
PB  - International Joint Conferences on Artificial Intelligence
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SP  - 4381
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UR  - https://ijcai.org/proceedings/2015/638
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