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Title:
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BALANCING INTRANSITIVE RELATIONSHIPS IN MOBA
GAMES USING DEEP REINFORCEMENT LEARNING |
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Author(s):
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Conor Stephens and Chris Exton |
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ISBN:
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978-989-8704-20-7 |
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Editors:
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Katherine Blashki |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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Deep Reinforcement Learning, Game Balance, Design |
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Type:
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Full |
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First Page:
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126 |
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Last Page:
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134 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Balanced intransitive relationships are critical to the depth of strategy and player retention within esports games. Intransitive
relationships comprise the metagame, a collection of strategies and play styles that are viable, each providing counterplay
for other viable strategies. This work presents a framework for testing the balance of massive online battle arena (MOBA)
games using deep reinforcement learning to identify the synergies between characters by measuring their effectiveness
against the other compositions within the games character roster. This research is designed for game designers and
developers to show how multi-agent reinforcement learning (MARL) can accelerate the balancing process and highlight
potential game-balance issues during the development process. Our findings conclude that accurate measurements of game
balance can be found with under 10 hours of simulation and show imbalances that traditional cost curve analysis approaches
failed to capture. Furthermore, we discovered that this approach reduced imbalance in each character's win rate by 20% in
our example project a key measurement that would be impossible to measure without collecting data from hundreds of
human-controlled games previously. The project's source code is publicly available at https://github.com/Taikatou/topdown-
shooter. |
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