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Learning Macromanagement in Starcraft by Deep Reinforcement Learning.

Wenzhen Huang1,2, Qiyue Yin1,2, Junge Zhang1,2

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This study introduces Mean Asynchronous Advantage Actor-Critic (MA3C), a novel deep reinforcement learning method for StarCraft macromanagement. MA3C significantly improves win rates against both weaker and stronger AI opponents.

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Area of Science:

  • Artificial Intelligence
  • Game Theory
  • Machine Learning

Background:

  • StarCraft presents complex challenges for AI research, particularly in macromanagement.
  • Effective macromanagement requires strategic unit selection based on dynamic game states.
  • Existing approaches often demand extensive expert knowledge.

Purpose of the Study:

  • To develop a reinforcement learning (RL) approach for StarCraft macromanagement.
  • To reduce reliance on expert knowledge and improve AI coordination.
  • To introduce a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C).

Main Methods:

  • Proposed Mean Asynchronous Advantage Actor-Critic (MA3C), a deep reinforcement learning algorithm.
  • MA3C computes approximate expected policy gradients to reduce variance.
  • Incorporated recurrent neural networks to handle imperfect information via history encoding.

Main Results:

  • MA3C achieved a win rate of approximately 90% against weaker opponents.
  • MA3C improved win rates by about 30% against stronger opponents.
  • Developed a novel visualization method to interpret the learned policy.

Conclusions:

  • The learned macromanagement policy effectively adapts to game rules and opponent strategies.
  • MA3C demonstrates strong cooperation with other AI modules.
  • The proposed method offers a promising direction for AI in complex real-time strategy games.