CLS-DP distills privileged multi-agent dynamics into a collaborative latent space that each agent infers from local RGB observations to condition diffusion-based actions, achieving 38% mean success on six RoboFactory tasks versus 20% for the best centralized baseline.
An initial introduction to cooperative multi-agent reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in cooperative MARL.
citing papers explorer
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Distilling Collaborative Dynamics into Latent Space for Implicit Coordination in Decentralized Multi-Agent Manipulation
CLS-DP distills privileged multi-agent dynamics into a collaborative latent space that each agent infers from local RGB observations to condition diffusion-based actions, achieving 38% mean success on six RoboFactory tasks versus 20% for the best centralized baseline.
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Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in cooperative MARL.