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This paper introduces PLATO, a pointer-network-based actor with a graph neural network critic for multi-agent reinforcement learning that handles both agent and task openness without retraining, evaluated in a wildfire suppression domain.
Jensen Huang and Harrison Chase discuss why companies need open agent systems and introduce the NemoClaw blueprint for LangChain Deep Agents.