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Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models

Dinger, Steven
http://orcid.org/0000-0002-8735-2652
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035

Abstract Details

2019, PhD, University of Cincinnati, Business: Business Administration.
Reinforcement learning has become a popular research topic due to the recent successes in combining deep learning value function estimation and reinforcement learning. Because of the popularity of these methods, deep learning has become the de facto standard for function approximation in reinforcement learning. However, other function approximation methods offer advantages in speed, ease of use, interpretability and stability. In our first essay, we examine several existing reinforcement learning methods that use decision trees for function approximation. Results of testing on a benchmark reinforcement learning problem show promising results for decision tree based methods. In addition, we propose the use of online random forests for reinforcement learning which show competitive results. In the second essay, we discuss accelerated boosting of partially linear models. Partially linear additive models are a powerful and flexible technique for modeling complex data. However, automatic variable selection to linear, nonlinear and uninformative terms can be computationally expensive. We propose using accelerated twin boosting to automatically select these terms and fit a partially linear additive model. Acceleration reduces the computational effort versus non-accelerated methods while maintaining accuracy and ease of use. Twin boosting is adopted to improve variable selection of accelerated boosting. We demonstrate the results of our proposed method on simulated and real data sets. We show that accelerated twin boosting results in accurate, parsimonious models with substantially less computation than non-accelerated twin boosting.
Uday Rao, Ph.D. (Committee Chair)
Raj Bhatnagar, Ph.D. (Committee Member)
Dungang Liu, Ph.D. (Committee Member)
Yan Yu, Ph.D. (Committee Member)
97 p.
Statistics
Fitted Q-Iteration; Gradient Boosting; Online Random Forest; Q-Learning; Twin Boosting; Variable Selection

Recommended Citations

Citations

  • Dinger, S. (2019). Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035

    APA Style (7th edition)

  • Dinger, Steven. Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models. 2019. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035.

    MLA Style (8th edition)

  • Dinger, Steven. "Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models." Doctoral dissertation, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035

    Chicago Manual of Style (17th edition)

ucin1562923541849035
371
© 2019, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.