Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models
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University of Cincinnati / OhioLINK
2019
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin15629235418490352021-08-03T07:11:43Z Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models Dinger, Steven Statistics Fitted Q-Iteration Gradient Boosting Online Random Forest Q-Learning Twin Boosting Variable Selection 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. 2019-10-01 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Statistics Fitted Q-Iteration Gradient Boosting Online Random Forest Q-Learning Twin Boosting Variable Selection |
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Statistics Fitted Q-Iteration Gradient Boosting Online Random Forest Q-Learning Twin Boosting Variable Selection Dinger, Steven Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models |
| author |
Dinger, Steven |
| author_facet |
Dinger, Steven |
| author_sort |
Dinger, Steven |
| title |
Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models |
| title_short |
Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models |
| title_full |
Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models |
| title_fullStr |
Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models |
| title_full_unstemmed |
Essays on Reinforcement Learning with Decision Trees and Accelerated Boosting of Partially Linear Additive Models |
| title_sort |
essays on reinforcement learning with decision trees and accelerated boosting of partially linear additive models |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2019 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923541849035 |
| work_keys_str_mv |
AT dingersteven essaysonreinforcementlearningwithdecisiontreesandacceleratedboostingofpartiallylinearadditivemodels |
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1719455950394359808 |