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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.
Mamba's linear complexity lets you ditch computationally intensive graph attention for flexible job shop scheduling, achieving state-of-the-art performance with faster solving speeds.
