Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163306
Title: Deep reinforcement learning for real world problems
Authors: Wee, Andrew Chin Ho
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Wee, A. C. H. (2022). Deep reinforcement learning for real world problems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163306
Abstract: Dota 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.
URI: https://hdl.handle.net/10356/163306
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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