ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, 2023.
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Brac University
2024
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| Acceso en línea: | http://hdl.handle.net/10361/23385 |
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10361-233852024-06-11T21:04:12Z ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network Dutta, Amit Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Reinforced learning Recurrent neural network (RNN) OpenAiGym Proximal policy optimization Blockchain Machine learning Blockchains (Databases) Computer security Cryptocurrencies This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-47). Blockchain 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. Amit Dutta M.Sc. in Computer Science 2024-06-11T09:41:56Z 2024-06-11T09:41:56Z ©2023 2023-07 Thesis ID 21166028 http://hdl.handle.net/10361/23385 en Brac 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. 58 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Reinforced learning Recurrent neural network (RNN) OpenAiGym Proximal policy optimization Blockchain Machine learning Blockchains (Databases) Computer security Cryptocurrencies |
| spellingShingle |
Reinforced learning Recurrent neural network (RNN) OpenAiGym Proximal policy optimization Blockchain Machine learning Blockchains (Databases) Computer security Cryptocurrencies Dutta, Amit ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, 2023. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Dutta, Amit |
| format |
Thesis |
| author |
Dutta, Amit |
| author_sort |
Dutta, Amit |
| title |
ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network |
| title_short |
ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network |
| title_full |
ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network |
| title_fullStr |
ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network |
| title_full_unstemmed |
ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network |
| title_sort |
robb: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23385 |
| work_keys_str_mv |
AT duttaamit robbrecurrentproximalpolicyoptimizationreinforcementlearningforoptimalblockformationinbitcoinblockchainnetwork |
| _version_ |
1814308576646135808 |