AI RESEARCH
Scalable Neighborhood-Based Multi-Agent Actor-Critic
arXiv CS.LG
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ArXi:2604.18190v1 Announce Type: new We propose MADDPG-K, a scalable extension to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that addresses the computational limitations of centralized critic approaches. Centralized critics, which condition on the observations and actions of all agents, have nstrated significant performance gains in cooperative and competitive multi-agent settings. However, their critic networks grow linearly in input size with the number of agents, making them increasingly expensive to train at scale.