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Found 4 papers, 0 papers with code
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Second, we introduce a heterogeneous layer for decision-making, whose parameters are specifically generated by the learned latent variables.
Embodied LLM Agents Learn to Cooperate in Organized Teams
Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks.
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism
However, existing works either broadcast the messages leading to information redundancy, or learn targeted communication by modeling all the other agents as targets, which is not scalable when the number of agents varies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SSAN: Separable Self-Attention Network for Video Representation Learning
However, spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning.
