Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163306
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dc.contributor.authorWee, Andrew Chin Hoen_US
dc.date.accessioned2022-12-01T01:53:49Z-
dc.date.available2022-12-01T01:53:49Z-
dc.date.issued2022-
dc.identifier.citationWee, A. C. H. (2022). Deep reinforcement learning for real world problems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163306en_US
dc.identifier.urihttps://hdl.handle.net/10356/163306-
dc.description.abstractDota 2 is a popular Multiplayer Online Battle Arena (MOBA) video game. As an Esport, Dota 2 has a prize pool of over USD$40 million in 2021 for its annual flagship competition. Strategy plays a vital role in determining the outcome of games, and teams are constantly looking for means to gain a competitive edge. This work attempts to explore prediction models based solely on the team compositions at the start of a game. In essence, it attempts to predict which team is favoured before actual gameplay begins. Thereafter, we attempt to train and evaluate an AI agent to play the drafting game using Monte Carlo Tree Search. We use data from real matches obtained from the STRATZ API endpoint.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDeep reinforcement learning for real world problemsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorBo Anen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailboan@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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