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Distributed Actor-Critic Algorithms for Multiagent Reinforcement Learning Over Directed Graphs.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Distributed Systems

    Background:

    • Existing actor-critic (AC) methods for cooperative multiagent reinforcement learning (MARL) over graphs typically require undirected communication graphs and doubly stochastic weight matrices.
    • These limitations restrict the applicability of AC methods in scenarios with directed communication or less constrained network structures.

    Purpose of the Study:

    • To develop novel distributed AC algorithms for MARL that can operate effectively over directed graphs with fixed or changing topologies.
    • To relax the stringent conditions on graph structure and weight matrices required by previous AC-based MARL approaches.

    Main Methods:

    • Proposed a distributed AC algorithm for MARL over directed graphs with fixed topology, requiring only row stochastic weight matrices.
    • Developed a second distributed AC algorithm utilizing the push-sum protocol for MARL over directed graphs with changing topologies, requiring only column stochastic weight matrices.
    • Proved convergence for both algorithms using linear function approximation of the action-value function.

    Main Results:

    • The proposed algorithms demonstrate effectiveness in distributed MARL settings over directed graphs.
    • The algorithms successfully handle both fixed and changing network topologies.
    • Convergence is theoretically established for linear function approximation, supported by simulation results.

    Conclusions:

    • The developed AC algorithms significantly advance distributed MARL by accommodating directed graphs and less restrictive matrix properties.
    • These methods offer more flexible and broadly applicable solutions for cooperative MARL in dynamic and directed communication environments.