Beijing Zhongke Journal Publising Co. Ltd. Many real-world multi-agent scenarios can be naturally modeled as partially observable cooperative multi-agent reinforcement learning (MARL) problems. Agents ...
In mission-critical environments—think disaster response, financial systems, or supply chain logistics—success hinges on the seamless collaboration of multiple agents, whether they’re humans, machines ...
Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...
The industry hype says "more agents is all you need," but new data shows that strictly sequential tasks and tool-heavy integrations fail at scale.
Tech industry visionaries foresee a fundamental shift in network intelligence. Microsoft CEO Satya Nadella envisions humans collaborating with AI agent swarms, while Nvidia CEO Jensen Huang projects a ...
Robotic shepherding and multi-agent systems represent a frontier in the field of autonomous control, where bio‐inspired methodologies and advanced algorithms converge to direct groups of agents in a ...
Researchers at Google and MIT have conducted a comprehensive analysis of agentic systems and the dynamics between the number of agents, coordination structure, model capability, and task properties.
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