StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search

• URL: http://arxiv.org/abs/2006.10525v1
• Date: Fri, 12 Jun 2020 08:53:52 GMT
• Title: StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search
• Authors: Islam Elnabarawy, Kristijana Arroyo, Donald C. Wunsch II
• Abstract summary: This study examines the use of an agent based on the Monte-Carlo Tree Search algorithm for optimizing the build order in StarCraft II. It discusses how its performance can be improved even further by combining it with a deep reinforcement learning neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The real-time strategy game of StarCraft II has been posed as a challenge for reinforcement learning by Google's 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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