Unverified50% confidenceFactExact time
In heterogeneous asynchronous MMAB, if players are processed in decreasing order of their activation probabilities, the approximation ratio of the greedy algorithm can be improved to 1/√e.
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50%
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Long-term
Relevance
10/14/2026
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Related Claims
UnverifiedFor heterogeneous asynchronous MMAB, a polynomial-time greedy algorithm can achieve a 1/e approximation ratio relative to the optimal centralized assignment.77% similarUnverifiedThe ε-greedy policy selects actions randomly with probability ε and selects the action with the highest current Q-value with probability 1-ε; ε is typically set to 1.0 at the beginning of training and then linearly decayed to 0.01 or 0.1 over millions of frames.72% similarUnverifiedOnce the players' activation probabilities differ, the computation of the optimal assignment had previously remained an open problem.72% similarUnverifiedWhen all players' activation probabilities are equal, this is the homogeneous case; when the activation probabilities differ from one another, it is called heterogeneous.71% similar
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