Presentation Title
P-26 Application of Approximate Q-Learning to Simplified Macromanagement in StarCraft II
Presenter Status
J. N. Andrews Honors Scholar
Second Presenter Status
Assistant Professor, Department of Computing
Preferred Session
Poster Session
Location
Buller Hall Hallways
Start Date
22-10-2021 2:00 PM
End Date
22-10-2021 3:00 PM
Presentation Abstract
Contemporary research in Machine Learning in regards to StarCraft II has recently utilized the power of both neural networks and reinforcement learning in the form of “Deep Reinforcement Learning,” and has risen greatly in popularity. Unfortunately, the use of neural networks comes with great costs in resources and requires expensive hardware to run in a manageable amount of time. Instead, we propose the use of a modified form Approximate Q-learning and forego the use of neural networks to explore the performance of non-neural network strategies in the StarCraft II environment in regards to outpacing an enemy in simplified macromanagement gameplay.
P-26 Application of Approximate Q-Learning to Simplified Macromanagement in StarCraft II
Buller Hall Hallways
Contemporary research in Machine Learning in regards to StarCraft II has recently utilized the power of both neural networks and reinforcement learning in the form of “Deep Reinforcement Learning,” and has risen greatly in popularity. Unfortunately, the use of neural networks comes with great costs in resources and requires expensive hardware to run in a manageable amount of time. Instead, we propose the use of a modified form Approximate Q-learning and forego the use of neural networks to explore the performance of non-neural network strategies in the StarCraft II environment in regards to outpacing an enemy in simplified macromanagement gameplay.