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Policy optimization for industrial benchmark using deep reinforcement learning

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dc.contributor.authorKumar, Anurag, author
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorChitsaz, Hamid, committee member
dc.contributor.authorKirby, Michael, committee member
dc.date.accessioned2020-09-07T10:08:49Z
dc.date.available2020-09-07T10:08:49Z
dc.date.issued2020
dc.description.abstractSignificant advancements have been made in the field of Reinforcement Learning (RL) in recent decades. Numerous novel RL environments and algorithms are mastering these problems that have been studied, evaluated, and published. The most popular RL benchmark environments produced by OpenAI Gym and DeepMind Labs are modeled after single/multi-player board, video games, or single-purpose robots and the RL algorithms modeling optimal policies for playing those games have even outperformed humans in almost all of them. However, the real-world applications using RL is very limited, as the academic community has limited access to real industrial data and applications. Industrial Benchmark (IB) is a novel RL benchmark motivated by Industrial Control problems with properties such as continuous state and action spaces, high dimensionality, partially observable state space, delayed effects combined with complex heteroscedastic stochastic behavior. We have used Deep Reinforcement Learning (DRL) algorithms like Deep Q-Networks (DQN) and Double-DQN (DDQN) to study and model optimal policies on IB. Our empirical results show various DRL models outperforming previously published models on the same IB.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierKumar_colostate_0053N_16226.pdf
dc.identifier.urihttps://hdl.handle.net/10217/212061
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectdeep reinforcement learning
dc.subjectindustrial benchmark
dc.subjectDDQN
dc.subjectq-learning
dc.subjectDQN
dc.titlePolicy optimization for industrial benchmark using deep reinforcement learning
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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