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Title:Efficient reinforcement learning through variance reduction and trajectory synthesis
Author(s):Zhao, Xiaoming
Advisor(s):Peng, Jian
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Machine Learning, Reinforcement Learning, Optimization
Abstract:Reinforcement learning is a general and unified framework that has been proven promising for many important AI applications, such as robotics, self-driving vehicles. However, current reinforcement learning algorithms suffer from large variance and sampling inefficiency, which leads to slow convergent rate as well as unstable performance. In this thesis, we manage to alleviate these two relevant problems. For enormous variance, we combine variance reduced optimization with deep Q-learning. For inefficient sampling, we propose novel framework that integrates self-imitation learning and artificial synthesis procedure. Our approaches, which are flexible and could be extended to many tasks, prove their effectiveness through experiments on Atari and MuJoCo environment.
Issue Date:2019-04-22
Rights Information:Copyright 2019 Xiaoming Zhao
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05

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