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    This study introduces a decentralized multiagent reinforcement learning (MARL) algorithm for partially observable Markov games (POMGs). The method enables agents to learn without central coordination, enhancing cooperative task performance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • Cooperative multiagent reinforcement learning (MARL) in partially observable Markov games (POMGs) presents challenges due to decentralized information.
    • Existing centralized training methods require global state and reward information, limiting scalability and applicability.

    Purpose of the Study:

    • To develop a fully decentralized MARL algorithm for POMGs.
    • To enable cooperative learning among agents without a central controller.
    • To adapt Trust Region Policy Optimization (TRPO) for decentralized MARL.

    Main Methods:

    • Extended TRPO to cooperative MARL for POMGs.
    • Transformed the TRPO policy update into a distributed consensus optimization for networked agents.
    • Proposed a decentralized MARL algorithm using a distributed alternating direction method of multipliers (ADMM) with local convexification and trust-region.
    • Agents communicate local policy ratios via a peer-to-peer network.

    Main Results:

    • The proposed algorithm effectively trains agents in a decentralized manner.
    • Demonstrated effectiveness in two cooperative environments.
    • Eliminated the need for a central control center to gather global information.

    Conclusions:

    • The decentralized ADMM-based MARL algorithm is effective for cooperative POMGs.
    • This approach offers a scalable alternative to centralized training methods in MARL.
    • Enables efficient learning in networked multiagent systems with partial observability.