Extension: Decentralized multi-agent reinforcement learning based on best-response policies
Decentralized multi-agent reinforcement learning based on best-response policies reports that most studies use fully centralized learning to ease transfer from single-agent systems. This extends Kaal's centralization critique beyond federated-learning aggregators by identifying ease of methodological transfer as another centralization pressure. The abstract does not establish the communication-load or vulnerability effects Kaal identifies.
Affirmed commentary position. This record extends a source-bound scholarly claim but is not a verbatim paper claim.
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decentralizationhistorical-responsescholarly-literaturecrossref
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