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Value decomposition methods such as QMIX approximately solve the credit assignment problem by decomposing the joint Q-value function into the sum of individual Q-value functions for each agent, but their monotonicity assumption limits expressive power.
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9/11/2026
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UnverifiedThe QMIX algorithm decomposes the team's global Q-value into a monotonic mixture of each agent's local Q-values, ensuring that the globally optimal action is equivalent to each agent independently taking the maximum value77% similarUnverifiedQ-value updates follow the iterative form of the Bellman equation: Q(s,a) ← Q(s,a) + α[r + γ·max Q(s',a') - Q(s,a)].65% similarUnverifiedQ-learning、DQN 等算法本质上都是在近似求解贝尔曼最优方程65% similarUnverified贝尔曼方程将长期累积奖励的优化问题分解为可递归求解的子问题，奠定了Q-learning、策略梯度等经典算法的理论根基63% similarUnverifiedDueling DQN的核心思想是将Q值分解为状态价值函数V(s)和动作优势函数A(s,a)两个独立流63% similar
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