在完全合作的多智能強化學習設定下，每個智能體僅能觀測到部分資訊且策略會不斷改變，因此導致環境具有高度隨機性。為了解決上述問題，我們提出了一個名為 DFAC 的統一框架，用於將分布式強化學習演算法和價值函數分解的方法相結合。該框架將價值函數分解方法推廣到能夠分解價值分布函數。為了驗證 DFAC 的有效性，我們首先展示它在具隨機獎勵的簡易矩陣遊戲中成功分解價值分布函數的能力。接著，我們在 StarCraft Multi-Agent Challenge 的所有 Super Hard 難度地圖，和六個自己設計的 Ultra Hard 地圖上進行實驗，結果顯示 DFAC 在分數上優於數個基準方法。

這篇研究論文已經被《機器學習研究期刊》（JMLR 2023）接受發表。

In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of other agents. To address the above issues, we proposed a unified framework, called DFAC, for integrating distributional RL with value function factorization methods. This framework generalizes expected value function factorization methods to enable the factorization of return distributions. To validate DFAC, we first demonstrate its ability to factorize the value functions of a simple matrix game with stochastic rewards. Then, we perform experiments on all Super Hard maps of the StarCraft Multi-Agent Challenge and six self-designed Ultra Hard maps, showing that DFAC is able to outperform a number of baselines.

This research paper has been accepted by the Journal of Machine Learning Research (JMLR 2023).

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