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Title Action selection in modular reinforcement learning
Publication Date
Date Accessioned
Degree MSin Computer Sciences
Discipline/Department Computer Sciences
Degree Level masters
University/Publisher University of Texas – Austin
Abstract Modular reinforcement learning is an approach to resolve the curse of dimensionality problem in traditional reinforcement learning. We design and implement a modular reinforcement learning algorithm, which is based on three major components: Markov decision process decomposition, module training, and global action selection. We define and formalize module class and module instance concepts in decomposition step. Under our framework of decomposition, we train each modules efficiently using SARSA($\lambda$) algorithm. Then we design, implement, test, and compare three action selection algorithms based on different heuristics: Module Combination, Module Selection, and Module Voting. For last two algorithms, we propose a method to calculate module weights efficiently, by using standard deviation of Q-values of each module. We show that Module Combination and Module Voting algorithms produce satisfactory performance in our test domain.
Subjects/Keywords Modular reinforcement learning; Action selection; Module weight
Contributors Ballard, Dana H. (Dana Harry), 1946- (advisor)
Language en
Country of Publication us
Record ID handle:2152/25916
Repository texas
Date Retrieved
Date Indexed 2020-10-15
Grantor The University of Texas at Austin
Note [] text; [department] Computer Sciences;

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