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Multi-Agent Reinforcement Learning in Games: Research and Applications.

Haiyang Li1, Ping Yang1, Weidong Liu1

  • 1High-Tech Institute of Xi'an, Xi'an 710038, China.

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Summary
This summary is machine-generated.

This study integrates multi-agent reinforcement learning (MARL) and game theory, inspired by biological systems. It enhances collective intelligence for complex decision-making in dynamic environments like smart cities.

Keywords:
evolutionary computationgame theorymulti-agent reinforcement learningstochastic games

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Area of Science:

  • Artificial Intelligence
  • Computational Game Theory
  • Bio-inspired Computing

Background:

  • Biological systems demonstrate self-organizing intelligence.
  • Bridging game-theoretic rationality and multi-agent adaptability is crucial for complex systems.
  • Existing frameworks lack integration of bio-inspired principles with advanced AI for collective decision-making.

Purpose of the Study:

  • To systematically review the convergence of multi-agent reinforcement learning (MARL) and game theory.
  • To elucidate the potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments.
  • To identify technical breakthroughs and map development pathways for enhancing multi-agent systems.

Main Methods:

  • Utilizing stochastic game and extensive-form game-theoretic frameworks.
  • Establishing a methodological taxonomy based on value function optimization, policy gradient learning, and online search planning.
  • Incorporating bio-inspired optimization approaches, including evolutionary computation and population-based learning.

Main Results:

  • Developed a methodological taxonomy clarifying algorithmic advancements in MARL and game theory.
  • Identified technical breakthroughs in MARL applications for smart city scenarios (e.g., intelligent transportation, UAV scheduling).
  • Highlighted the efficacy of bio-inspired mechanisms for dynamic strategy generation and exploration efficiency.

Conclusions:

  • The integration of MARL and game theory, enhanced by bio-inspired computing, offers significant potential for collective intelligence.
  • This interdisciplinary approach provides a roadmap for developing advanced multi-agent systems capable of optimal decision-making in complex, dynamic environments.
  • Findings reveal core principles for group decision-making and map technological development pathways.