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Register a new userStarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search
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Added by
Islam Elnabarawy
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The real-time strategy game of StarCraft II has been posed as a challenge for reinforcement learning by Googles DeepMind. This study examines the use of an agent based on the Monte-Carlo Tree Search algorithm for optimizing the build order in StarCraft II, and discusses how its performance can be improved even further by combining it with a deep reinforcement learning neural network. The experimental results accomplished using Monte-Carlo Tree Search achieves a score similar to a novice human player by only using very limited time and computational resources, which paves the way to achieving scores comparable to those of a human expert by combining it with the use of deep reinforcement learning.
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Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari. Previous research has developed parall
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