Ahoy enables LLM agents to select and enact multiple declarative interaction protocols concurrently without specialized training to achieve goals.
Asynchronous multi-agent reinforcement learning for efficient real-time multi-robot cooperative exploration
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SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.
citing papers explorer
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Ahoy: LLMs Enacting Multiagent Interaction Protocols
Ahoy enables LLM agents to select and enact multiple declarative interaction protocols concurrently without specialized training to achieve goals.
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Empowering Multi-Robot Cooperation via Sequential World Models
SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.