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dc.contributor.advisorAlam, Md. Golam Rabiul
dc.contributor.authorDutta, Amit
dc.date.accessioned2024-06-11T09:41:56Z
dc.date.available2024-06-11T09:41:56Z
dc.date.copyright©2023
dc.date.issued2023-07
dc.identifier.otherID 21166028
dc.identifier.urihttp://hdl.handle.net/10361/23385
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, 2023.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 45-47).
dc.description.abstractBlockchain is a ground-breaking technology that has changed how we manage and store protected data. It is a decentralized ledger that enables safe, open, and unchangeable record-keeping. It relies on a distributed network of nodes rather than a single central authority to check and verify transactions, guaranteeing that each entry is correct and unchangeable. Transactions in a blockchain network are grouped into blocks, which are then linked together in a chronological and immutable chain. Block size is a critical parameter in blockchain technology, which refers to the maximum size of each block in the chain. However, we cannot just change the block size of the blockchain. It is challenging and will create security issues. The Block size is crucial because it a↵ects the number of transactions processed per second, the confirmation time, and overall network efficiency. The confirmation time should be faster to ensure stable earnings for the miners. Moreover, it needs help with broader applications due to high transaction fees and long verification times. We have proposed a reinforcement learning model named ROBB that can efficiently create a block considering the current network state and previous transactions. At first, the problem was converted into a reinforcement learning environment to solve using multiple reinforcement algorithms. We developed a blockchain simulator to replicate the network environment. To transform it into a reinforcement learning environment, we integrated it with OpenAI Gym. The simulator was trained by generating random transactions. Finally, we designed a reward function that enables the simulator to hold transactions and create blocks with the pending transactions when it determines that the environment is favourable. In the final results, ROBB successfully minimized the waiting time for transactions and utilized the blocks to their full potential, which is crucial for private blockchains used in medical records. Additionally, it optimized the block space, building upon the findings of previous researchers.en_US
dc.description.statementofresponsibilityAmit Dutta
dc.format.extent58 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectReinforced learningen_US
dc.subjectRecurrent neural network (RNN)en_US
dc.subjectOpenAiGymen_US
dc.subjectProximal policy optimizationen_US
dc.subjectBlockchainen_US
dc.subjectMachine learningen_US
dc.subject.lcshBlockchains (Databases)
dc.subject.lcshComputer security
dc.subject.lcshCryptocurrencies
dc.titleROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain networken_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeM.Sc. in Computer Science


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