Regularization Methods for Improving Data Efficiency in Reinforcement Learning

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Ahmadian Shahreza, Hamid Reza | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54575 (02)
University: Sharif University of Technology
Department: Mathematical Sciences
Advisor(s): Alishahi, Kasra
Abstract:
Reinforcement learning is a successful model of learning that has received a lot of attention in recent years and has had significant achievements. However, methods based on reinforcement require a lot of data. Therefore, it is important to find ideas to keep learning at a high level despite the lack of data. Many of these ideas are known as statistical regularity. In this thesis, we study methods to enhance the learning rate, including methods for sharing neural network weights between value function and policy networks. In this thesis we will try to gain a more general understanding of the regularization in reinforcement learning and increase the learning rate by implementing these methods in a few specific practical examples.
Keywords:
Regularization ; Transfer Learning ; Reinforcement Learning ; Data Efficiency