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DVF:Multi-agent Q-learning with difference value factorization
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AI
DOI：10.1016/j.knosys.2024.111422.png)
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In cooperative multi -agent games, agents are required to learn effective cooperative behaviors within complex action spaces. An effective approach is to utilize the Individual -Global -Max (IGM) principle to decompose the global reward signal into individual contributions from each agent. However, existing methods confront two significant challenges: ensuring the accuracy of the value function decomposition process and guaranteeing monotonic improvement in joint policies. These challenges become exacerbated in scenarios involving nonmonotonic matrices. To address these challenges, we introduce a novel and flexible value factorization method called Difference Value Factorization (DVF). The key idea of our method is to transform the IGM principle into a new form DVF-IGM, which addresses non -monotonic constraints by ensuring the consistency of the IGM process between the joint difference value and the complex non-linear sum of independent difference values. A centralized evaluator is employed to estimate global Q -values, which not only enhances expressiveness but also constructs difference values for updating individual value functions. We demonstrate that DVF-IGM is an equivalent transformation of IGM and that DVF has the monotonic improvement property. Empirically, our method has been shown to maintain and recover the optimal policy in non -monotonic matrix games and achieve state-of-the-art performance in cooperative tasks within the StarCraft Multi -Agent Challenge (SMAC).
Keyword:
Multi-agent reinforcement learning
Value factorization
Individual-global-max
Reinforcement learning
Multi-agent system
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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