Multiagent Adversarial Collaborative Learning via Mean-Field Theory.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Multiagent Systems

Background:

• Multiagent reinforcement learning (MARL) faces scalability issues due to the curse of dimensionality and nonstationary environments, exacerbated by increasing agent numbers.
• Existing MARL methods struggle with complex interactions and simultaneous learning dynamics in cooperative-competitive settings.

Purpose of the Study:

• To propose a novel adversarial collaborative learning method to overcome the limitations of traditional MARL in large-scale systems.
• To enhance learning efficiency and stability in mixed cooperative-competitive environments with numerous agents.

Main Methods:

• Developed an adversarial collaborative learning framework leveraging friend-or-foe Q-learning and mean-field theory.
• Transformed the multiagent Markov game into a two-player zero-sum game by considering neighbors as friend and opponent coalitions.
• Employed adversarial max and min steps with neural networks to optimize coalition mean effects and individual agent best responses, converging to a Nash equilibrium.

Main Results:

• The proposed method effectively simplifies complex agent interactions by utilizing mean-field theory.
• Experimental results on two platforms demonstrate significant learning effectiveness and robustness, particularly in scenarios with a large number of agents.
• The adversarial training process ensures convergence to a Nash equilibrium, enhancing stability and performance.

Conclusions:

• The adversarial collaborative learning approach offers a scalable and effective solution for MARL challenges.
• This method shows strong performance in mixed cooperative-competitive environments, outperforming traditional approaches with increased agent populations.
• The integration of mean-field theory and adversarial training provides a promising direction for future MARL research.