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Multi-agent RL agents can learn to collaborate *faster* by actively "perceiving" and aligning with each other's policy updates, rather than passively observing environment interactions.
Second-order federated learning can be made robust and practical: FedRCO overcomes instability issues and outperforms first-order methods in non-IID settings.
