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    Area of Science:

    • Game Theory
    • Artificial Intelligence
    • Network Engineering

    Background:

    • Investigates the Nash-seeking problem in infinite network aggregative Markov games.
    • Focuses on a noncooperative framework where agents maximize rewards without complete environmental or reward function knowledge.

    Purpose of the Study:

    • Develop a continuous multiagent reinforcement learning (MARL) algorithm for the Nash-seeking problem in infinite dynamic games.
    • Provide a convergence guarantee for the proposed algorithm.

    Main Methods:

    • Propose an actor-critic MARL algorithm utilizing expected policy gradient (EPG).
    • Employ two general function approximators for estimating value functions and Nash policies.
    • Address continuous state and action spaces and use a novel EPG to reduce gradient approximation variance.

    Main Results:

    • Prove convergence of agent policies to a unique Nash equilibrium (NE) under conventional assumptions like linear function approximators.
    • Conduct an estimation error analysis to understand the impact of function approximation errors.

    Conclusions:

    • The developed MARL algorithm effectively guides agents towards the Nash equilibrium in complex, non-cooperative games.
    • Demonstrate the framework's applicability through a case study in cloud radio access networks (C-RAN).