<ul><li>The Potentially Optimal Joint Actions Weighted QMIX (POWQMIX) algorithm is proposed as an improvement to value function factorization methods in cooperative multi-agent reinforcement learning.</li><li>POWQMIX recognizes potentially optimal joint actions and assigns higher weights to corresponding losses during training, increasing the representation capacity of value factorization compared to existing methods.</li><li>The algorithm guarantees to recover the optimal policy through its weighted training approach.</li><li>Experiments in various environments demonstrate that POWQMIX outperforms state-of-the-art value-based multi-agent reinforcement learning methods.</li></ul>

POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning

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