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Multi-agent reinforcement learning with approximate model learning for competitive games.

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  • 1School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea.

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Summary
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This study introduces a novel method for training cooperative multi-agent systems (MAS) to compete against adversaries. The approach enhances learning efficiency and goal achievement in competitive environments.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Multi-Agent Systems

Background:

  • Multi-agent systems (MAS) often face challenges in competitive environments due to nonstationarity and the need for effective cooperation.
  • Existing methods may require access to opponent models or struggle with dynamic environments.

Purpose of the Study:

  • To develop a method for learning cooperative multi-agent policies against multiple opponents.
  • To enhance learning efficiency and goal achievement in competitive MAS.

Main Methods:

  • Utilizes recurrent neural network-based actor-critic networks and deterministic policy gradients.
  • Agents communicate via forward and backward paths, trained independently of opponents.
  • Employs approximate model learning with auxiliary prediction networks to handle nonstationarity.

Main Results:

  • Demonstrated superiority over alternative methods in competitive multi-agent environments.
  • Achieved higher learning efficiency and better goal achievements.
  • The proposed method effectively handles nonstationarity and promotes agent cooperation.

Conclusions:

  • The proposed method offers a robust solution for training agents in competitive multi-agent settings.
  • Independent training and communication mechanisms contribute to superior performance.
  • Approximate model learning is crucial for addressing dynamic opponent behaviors.