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Independent policy composition in multi-agent systems can lead to worse outcomes than any individual policy in the library, challenging conventional wisdom in reinforcement learning.
Normalizing dual-encoder networks not only clarifies their interpretability but also reveals hidden structure in learned representations, challenging existing assumptions in the field.
Aggregating rewards in the advantage while keeping likelihood ratios per-agent can significantly enhance cooperative multi-agent learning performance.
