Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints

In three linesIC-Q algorithm for decentralized multi-agent workflow learning under interface constraints. Each agent observes only a local function of shared artifact and private state, with no centralized access to joint trajectories. Finite-sample convergence guarantee for neural Q-learning under decentralized partial observability.

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