Tianyi Zhao
Software Development
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Reinforcement Learning Agent for StarCraft II Mini-Games
My thesis aimed to build reinforcement spent six months on this project, which
games. When I built the agents in my and reinforcement learning algorithms.
learning agents for StarCraft II mini-
project, I used two different reinforcement
learning algorithms. One of the algorithms An agent which can learn and practice
(A3C), which is state-of-the-art, and the Strategy games. It can help developers
is Asynchronous Advantage Actor-Critic
other is Deep Q-Learning. Both agents
can learn and play all StarCraft II mini-
games without changing any parameter.
I chose this topic because I am very
passionate about machine learning in
gaming and I am a big fan of StarCraft. I
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gave me an opportunity to learn Python
SOFTWARE DEVELOPMENT
with itself is a great tool for Real-Time
balance the game and explore more new
strategies.